{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "environ['train_device'] = 'cuda:3' # training device: 'cpu' or 'cuda:X'\n",
    "environ['store_device'] = 'cuda:3' # Data storing device:  'cpu' or 'cuda:X'\n",
    "\n",
    "train_val_dataset_file = '/data/kk4796/datasets/dataset_13M_train_val.pkl' \n",
    "test_dataset_file = '/data/kk4796/datasets/dataset_13M_test.pkl'\n",
    "benchmark_dataset_file ='/data/mm12191/datasets/benchmarks_ds2.json' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1st model of Tiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run utils_Tiling1_RLSTM.py # imports and defines some utils functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "<a id='load_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data filtering\n",
    "def filter_schedule(schedule_str): # needs to return True if we want the passed schedule to be dropped \n",
    "    \n",
    "    regex = \"T\\d\\([L\\d,]+\\)$\"\n",
    "    if re.search(regex, schedule_str): # drops all the schedules except the ones ending with tiling\n",
    "        return True\n",
    "    if schedule_str==\"\":\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data from  /data/kk4796/datasets/dataset_13M_train_val.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89655/89655 [15:56<00:00, 93.75it/s]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 259\n",
      "Number of batches dropped due to too much memory accesses:14306\n",
      "Data loaded\n",
      "Sizes: (25, 234) batches\n",
      "loading data from  /data/kk4796/datasets/dataset_13M_test.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9961/9961 [01:32<00:00, 108.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 31\n",
      "Number of batches dropped due to too much memory accesses:1414\n",
      "Data loaded\n",
      "Sizes: (31, 0) batches\n",
      "loading data from  /data/mm12191/datasets/benchmarks_ds2.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:01<00:00, 29.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 3\n",
      "Data loaded\n",
      "Sizes: (3, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_val_dataset, val_bl, val_indices, train_bl, train_indices = load_data(train_val_dataset_file,split_ratio=0.1, max_batch_size=2048, filter_func=filter_schedule)\n",
    "test_dataset, test_bl, test_indices, _, _ = load_data(test_dataset_file,split_ratio=1, max_batch_size=2048, filter_func=filter_schedule)\n",
    "bench_dataset, bench_bl, bench_indices, _, _ = load_data(benchmark_dataset_file,split_ratio=1, max_batch_size=2048, filter_func=filter_schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 1272  # size of all the vectors\n",
    "\n",
    "model = None \n",
    "model = Model_Recursive_LSTM_v2(input_size, comp_embed_layer_sizes=[600, 900, 600, 400, 200], drops=[0.275, 0.4, 0.275, 0.175, 0.175] , output_size=28)\n",
    "model.to(train_device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = AdamW(model.parameters(),weight_decay=0.375e-2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000:  train Loss: 165.6899   val Loss: 125.2343   time: 5.78s   best: 125.2343\n",
      "Epoch 2/1000:  train Loss: 130.3229   val Loss: 121.8922   time: 1.91s   best: 121.8922\n",
      "Epoch 3/1000:  train Loss: 126.2805   val Loss: 120.2010   time: 1.92s   best: 120.2010\n",
      "Epoch 4/1000:  train Loss: 124.2409   val Loss: 118.7618   time: 1.92s   best: 118.7618\n",
      "Epoch 5/1000:  train Loss: 122.5425   val Loss: 117.2907   time: 1.93s   best: 117.2907\n",
      "Epoch 6/1000:  train Loss: 120.8894   val Loss: 115.6231   time: 1.93s   best: 115.6231\n",
      "Epoch 7/1000:  train Loss: 118.8512   val Loss: 113.7113   time: 1.93s   best: 113.7113\n",
      "Epoch 8/1000:  train Loss: 116.2696   val Loss: 110.3264   time: 1.92s   best: 110.3264\n",
      "Epoch 9/1000:  train Loss: 112.2088   val Loss: 104.8340   time: 1.91s   best: 104.8340\n",
      "Epoch 10/1000:  train Loss: 107.5028   val Loss: 99.8493   time: 1.90s   best: 99.8493\n",
      "Epoch 11/1000:  train Loss: 103.7491   val Loss: 96.8911   time: 1.90s   best: 96.8911\n",
      "Epoch 12/1000:  train Loss: 101.0878   val Loss: 94.7329   time: 1.89s   best: 94.7329\n",
      "Epoch 13/1000:  train Loss: 99.3875   val Loss: 92.6996   time: 1.89s   best: 92.6996\n",
      "Epoch 14/1000:  train Loss: 97.6314   val Loss: 91.2286   time: 1.89s   best: 91.2286\n",
      "Epoch 15/1000:  train Loss: 96.2608   val Loss: 90.0730   time: 1.89s   best: 90.0730\n",
      "Epoch 16/1000:  train Loss: 94.7770   val Loss: 89.6982   time: 1.89s   best: 89.6982\n",
      "Epoch 17/1000:  train Loss: 93.8929   val Loss: 88.8135   time: 1.89s   best: 88.8135\n",
      "Epoch 18/1000:  train Loss: 93.1989   val Loss: 88.4117   time: 1.89s   best: 88.4117\n",
      "Epoch 19/1000:  train Loss: 92.4395   val Loss: 87.8669   time: 1.89s   best: 87.8669\n",
      "Epoch 20/1000:  train Loss: 91.5607   val Loss: 87.3263   time: 1.89s   best: 87.3263\n",
      "Epoch 21/1000:  train Loss: 90.6719   val Loss: 87.8112   time: 1.89s   best: 87.3263\n",
      "Epoch 22/1000:  train Loss: 90.0540   val Loss: 87.0788   time: 1.90s   best: 87.0788\n",
      "Epoch 23/1000:  train Loss: 89.1405   val Loss: 85.3965   time: 1.92s   best: 85.3965\n",
      "Epoch 24/1000:  train Loss: 88.2157   val Loss: 85.1139   time: 1.93s   best: 85.1139\n",
      "Epoch 25/1000:  train Loss: 87.5038   val Loss: 84.3013   time: 1.93s   best: 84.3013\n",
      "Epoch 26/1000:  train Loss: 86.4742   val Loss: 83.0885   time: 1.93s   best: 83.0885\n",
      "Epoch 27/1000:  train Loss: 85.7005   val Loss: 81.9288   time: 1.93s   best: 81.9288\n",
      "Epoch 28/1000:  train Loss: 85.0660   val Loss: 81.5741   time: 1.93s   best: 81.5741\n",
      "Epoch 29/1000:  train Loss: 84.5128   val Loss: 81.4175   time: 1.91s   best: 81.4175\n",
      "Epoch 30/1000:  train Loss: 83.6495   val Loss: 80.2098   time: 1.89s   best: 80.2098\n",
      "Epoch 31/1000:  train Loss: 83.0864   val Loss: 79.3842   time: 1.90s   best: 79.3842\n",
      "Epoch 32/1000:  train Loss: 82.6756   val Loss: 79.3077   time: 1.89s   best: 79.3077\n",
      "Epoch 33/1000:  train Loss: 81.8323   val Loss: 78.3796   time: 1.89s   best: 78.3796\n",
      "Epoch 34/1000:  train Loss: 81.0631   val Loss: 77.2526   time: 1.89s   best: 77.2526\n",
      "Epoch 35/1000:  train Loss: 80.3294   val Loss: 76.3732   time: 1.90s   best: 76.3732\n",
      "Epoch 36/1000:  train Loss: 79.4227   val Loss: 75.2722   time: 1.93s   best: 75.2722\n",
      "Epoch 37/1000:  train Loss: 78.6855   val Loss: 74.3041   time: 1.93s   best: 74.3041\n",
      "Epoch 38/1000:  train Loss: 77.8010   val Loss: 73.7237   time: 1.93s   best: 73.7237\n",
      "Epoch 39/1000:  train Loss: 77.0250   val Loss: 73.1154   time: 1.93s   best: 73.1154\n",
      "Epoch 40/1000:  train Loss: 76.0645   val Loss: 71.7732   time: 1.93s   best: 71.7732\n",
      "Epoch 41/1000:  train Loss: 75.1706   val Loss: 70.5224   time: 1.93s   best: 70.5224\n",
      "Epoch 42/1000:  train Loss: 74.3155   val Loss: 69.6791   time: 1.90s   best: 69.6791\n",
      "Epoch 43/1000:  train Loss: 73.6640   val Loss: 68.7501   time: 1.90s   best: 68.7501\n",
      "Epoch 44/1000:  train Loss: 72.8475   val Loss: 68.0154   time: 1.89s   best: 68.0154\n",
      "Epoch 45/1000:  train Loss: 72.2998   val Loss: 67.1445   time: 1.88s   best: 67.1445\n",
      "Epoch 46/1000:  train Loss: 71.5369   val Loss: 66.8866   time: 1.88s   best: 66.8866\n",
      "Epoch 47/1000:  train Loss: 71.1904   val Loss: 66.1899   time: 1.89s   best: 66.1899\n",
      "Epoch 48/1000:  train Loss: 70.5990   val Loss: 65.4654   time: 1.89s   best: 65.4654\n",
      "Epoch 49/1000:  train Loss: 69.9344   val Loss: 64.8854   time: 1.91s   best: 64.8854\n",
      "Epoch 50/1000:  train Loss: 69.5370   val Loss: 64.4961   time: 1.92s   best: 64.4961\n",
      "Epoch 51/1000:  train Loss: 68.9983   val Loss: 63.7330   time: 1.93s   best: 63.7330\n",
      "Epoch 52/1000:  train Loss: 68.6548   val Loss: 63.3070   time: 1.93s   best: 63.3070\n",
      "Epoch 53/1000:  train Loss: 68.1964   val Loss: 62.8017   time: 1.93s   best: 62.8017\n",
      "Epoch 54/1000:  train Loss: 67.7480   val Loss: 62.5441   time: 1.92s   best: 62.5441\n",
      "Epoch 55/1000:  train Loss: 67.2927   val Loss: 61.9984   time: 1.92s   best: 61.9984\n",
      "Epoch 56/1000:  train Loss: 66.9254   val Loss: 61.6424   time: 1.90s   best: 61.6424\n",
      "Epoch 57/1000:  train Loss: 66.6275   val Loss: 61.6665   time: 1.90s   best: 61.6424\n",
      "Epoch 58/1000:  train Loss: 66.2897   val Loss: 60.9435   time: 1.89s   best: 60.9435\n",
      "Epoch 59/1000:  train Loss: 66.0054   val Loss: 60.7677   time: 1.89s   best: 60.7677\n",
      "Epoch 60/1000:  train Loss: 65.6749   val Loss: 60.3188   time: 1.89s   best: 60.3188\n",
      "Epoch 61/1000:  train Loss: 65.2727   val Loss: 60.3492   time: 1.90s   best: 60.3188\n",
      "Epoch 62/1000:  train Loss: 65.1743   val Loss: 59.9853   time: 1.92s   best: 59.9853\n",
      "Epoch 63/1000:  train Loss: 64.6664   val Loss: 59.6974   time: 1.93s   best: 59.6974\n",
      "Epoch 64/1000:  train Loss: 64.5237   val Loss: 59.3334   time: 1.93s   best: 59.3334\n",
      "Epoch 65/1000:  train Loss: 64.2373   val Loss: 59.1270   time: 1.94s   best: 59.1270\n",
      "Epoch 66/1000:  train Loss: 63.9936   val Loss: 59.0898   time: 1.93s   best: 59.0898\n",
      "Epoch 67/1000:  train Loss: 63.8201   val Loss: 58.8242   time: 1.93s   best: 58.8242\n",
      "Epoch 68/1000:  train Loss: 63.4366   val Loss: 58.5758   time: 1.91s   best: 58.5758\n",
      "Epoch 69/1000:  train Loss: 63.3350   val Loss: 58.3421   time: 1.91s   best: 58.3421\n",
      "Epoch 70/1000:  train Loss: 62.8938   val Loss: 57.8797   time: 1.90s   best: 57.8797\n",
      "Epoch 71/1000:  train Loss: 62.6021   val Loss: 57.5080   time: 1.89s   best: 57.5080\n",
      "Epoch 72/1000:  train Loss: 62.4812   val Loss: 57.4809   time: 1.89s   best: 57.4809\n",
      "Epoch 73/1000:  train Loss: 62.2923   val Loss: 57.2024   time: 1.89s   best: 57.2024\n",
      "Epoch 74/1000:  train Loss: 61.9973   val Loss: 57.1995   time: 1.92s   best: 57.1995\n",
      "Epoch 75/1000:  train Loss: 61.6849   val Loss: 56.6542   time: 1.93s   best: 56.6542\n",
      "Epoch 76/1000:  train Loss: 61.5091   val Loss: 56.5984   time: 1.93s   best: 56.5984\n",
      "Epoch 77/1000:  train Loss: 61.3346   val Loss: 56.4819   time: 1.93s   best: 56.4819\n",
      "Epoch 78/1000:  train Loss: 61.0401   val Loss: 55.9287   time: 1.93s   best: 55.9287\n",
      "Epoch 79/1000:  train Loss: 60.9254   val Loss: 56.1138   time: 1.93s   best: 55.9287\n",
      "Epoch 80/1000:  train Loss: 60.6271   val Loss: 55.8578   time: 1.92s   best: 55.8578\n",
      "Epoch 81/1000:  train Loss: 60.4457   val Loss: 55.5477   time: 1.91s   best: 55.5477\n",
      "Epoch 82/1000:  train Loss: 60.2418   val Loss: 55.3115   time: 1.90s   best: 55.3115\n",
      "Epoch 83/1000:  train Loss: 59.9383   val Loss: 55.2086   time: 1.90s   best: 55.2086\n",
      "Epoch 84/1000:  train Loss: 59.8009   val Loss: 55.0760   time: 1.89s   best: 55.0760\n",
      "Epoch 85/1000:  train Loss: 59.6458   val Loss: 54.9217   time: 1.89s   best: 54.9217\n",
      "Epoch 86/1000:  train Loss: 59.5326   val Loss: 54.8005   time: 1.89s   best: 54.8005\n",
      "Epoch 87/1000:  train Loss: 59.2767   val Loss: 54.7962   time: 1.91s   best: 54.7962\n",
      "Epoch 88/1000:  train Loss: 59.0469   val Loss: 54.4016   time: 1.93s   best: 54.4016\n",
      "Epoch 89/1000:  train Loss: 58.9529   val Loss: 54.3295   time: 1.93s   best: 54.3295\n",
      "Epoch 90/1000:  train Loss: 58.7050   val Loss: 54.1425   time: 1.93s   best: 54.1425\n",
      "Epoch 91/1000:  train Loss: 58.4314   val Loss: 53.9366   time: 1.94s   best: 53.9366\n",
      "Epoch 92/1000:  train Loss: 58.3426   val Loss: 53.7767   time: 1.93s   best: 53.7767\n",
      "Epoch 93/1000:  train Loss: 58.1758   val Loss: 53.4769   time: 1.92s   best: 53.4769\n",
      "Epoch 94/1000:  train Loss: 58.0264   val Loss: 53.2544   time: 1.91s   best: 53.2544\n",
      "Epoch 95/1000:  train Loss: 57.8416   val Loss: 53.3708   time: 1.91s   best: 53.2544\n",
      "Epoch 96/1000:  train Loss: 57.7323   val Loss: 53.0862   time: 1.90s   best: 53.0862\n",
      "Epoch 97/1000:  train Loss: 57.5280   val Loss: 52.9503   time: 1.89s   best: 52.9503\n",
      "Epoch 98/1000:  train Loss: 57.2920   val Loss: 52.8557   time: 1.89s   best: 52.8557\n",
      "Epoch 99/1000:  train Loss: 57.1716   val Loss: 52.6893   time: 1.89s   best: 52.6893\n",
      "Epoch 100/1000:  train Loss: 57.0923   val Loss: 52.6933   time: 1.91s   best: 52.6893\n",
      "Epoch 101/1000:  train Loss: 56.8084   val Loss: 52.3421   time: 1.92s   best: 52.3421\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/1000:  train Loss: 56.6452   val Loss: 52.3278   time: 1.94s   best: 52.3278\n",
      "Epoch 103/1000:  train Loss: 56.4468   val Loss: 52.0734   time: 1.94s   best: 52.0734\n",
      "Epoch 104/1000:  train Loss: 56.1898   val Loss: 51.7178   time: 1.94s   best: 51.7178\n",
      "Epoch 105/1000:  train Loss: 56.0689   val Loss: 51.6130   time: 1.94s   best: 51.6130\n",
      "Epoch 106/1000:  train Loss: 56.0283   val Loss: 51.5755   time: 1.93s   best: 51.5755\n",
      "Epoch 107/1000:  train Loss: 55.8110   val Loss: 51.3335   time: 1.92s   best: 51.3335\n",
      "Epoch 108/1000:  train Loss: 55.7650   val Loss: 51.2122   time: 1.91s   best: 51.2122\n",
      "Epoch 109/1000:  train Loss: 55.6111   val Loss: 51.1419   time: 1.90s   best: 51.1419\n",
      "Epoch 110/1000:  train Loss: 55.4638   val Loss: 51.2195   time: 1.90s   best: 51.1419\n",
      "Epoch 111/1000:  train Loss: 55.1751   val Loss: 50.7147   time: 1.90s   best: 50.7147\n",
      "Epoch 112/1000:  train Loss: 55.1702   val Loss: 50.8041   time: 1.90s   best: 50.7147\n",
      "Epoch 113/1000:  train Loss: 55.0232   val Loss: 50.4981   time: 1.92s   best: 50.4981\n",
      "Epoch 114/1000:  train Loss: 54.8860   val Loss: 50.4825   time: 1.94s   best: 50.4825\n",
      "Epoch 115/1000:  train Loss: 54.6998   val Loss: 50.2793   time: 1.94s   best: 50.2793\n",
      "Epoch 116/1000:  train Loss: 54.5894   val Loss: 50.1526   time: 1.94s   best: 50.1526\n",
      "Epoch 117/1000:  train Loss: 54.3064   val Loss: 49.9884   time: 1.94s   best: 49.9884\n",
      "Epoch 118/1000:  train Loss: 54.2372   val Loss: 49.6600   time: 1.94s   best: 49.6600\n",
      "Epoch 119/1000:  train Loss: 54.1289   val Loss: 49.5439   time: 1.92s   best: 49.5439\n",
      "Epoch 120/1000:  train Loss: 53.9770   val Loss: 49.6688   time: 1.90s   best: 49.5439\n",
      "Epoch 121/1000:  train Loss: 53.8729   val Loss: 49.3982   time: 1.91s   best: 49.3982\n",
      "Epoch 122/1000:  train Loss: 53.7422   val Loss: 49.2549   time: 1.90s   best: 49.2549\n",
      "Epoch 123/1000:  train Loss: 53.5424   val Loss: 49.1693   time: 1.90s   best: 49.1693\n",
      "Epoch 124/1000:  train Loss: 53.5053   val Loss: 49.2642   time: 1.90s   best: 49.1693\n",
      "Epoch 125/1000:  train Loss: 53.3277   val Loss: 48.6748   time: 1.90s   best: 48.6748\n",
      "Epoch 126/1000:  train Loss: 53.2234   val Loss: 48.7681   time: 1.91s   best: 48.6748\n",
      "Epoch 127/1000:  train Loss: 53.0621   val Loss: 48.8676   time: 1.93s   best: 48.6748\n",
      "Epoch 128/1000:  train Loss: 53.0528   val Loss: 48.7581   time: 1.94s   best: 48.6748\n",
      "Epoch 129/1000:  train Loss: 52.8103   val Loss: 48.5089   time: 1.94s   best: 48.5089\n",
      "Epoch 130/1000:  train Loss: 52.7117   val Loss: 48.2304   time: 1.94s   best: 48.2304\n",
      "Epoch 131/1000:  train Loss: 52.5426   val Loss: 48.2879   time: 1.94s   best: 48.2304\n",
      "Epoch 132/1000:  train Loss: 52.4047   val Loss: 48.2071   time: 1.92s   best: 48.2071\n",
      "Epoch 133/1000:  train Loss: 52.3828   val Loss: 48.0958   time: 1.92s   best: 48.0958\n",
      "Epoch 134/1000:  train Loss: 52.1771   val Loss: 48.1824   time: 1.93s   best: 48.0958\n",
      "Epoch 135/1000:  train Loss: 52.2383   val Loss: 48.1309   time: 1.91s   best: 48.0958\n",
      "Epoch 136/1000:  train Loss: 52.0329   val Loss: 47.9144   time: 1.91s   best: 47.9144\n",
      "Epoch 137/1000:  train Loss: 51.9171   val Loss: 47.6926   time: 1.91s   best: 47.6926\n",
      "Epoch 138/1000:  train Loss: 51.6845   val Loss: 47.5104   time: 1.91s   best: 47.5104\n",
      "Epoch 139/1000:  train Loss: 51.7431   val Loss: 47.8424   time: 1.93s   best: 47.5104\n",
      "Epoch 140/1000:  train Loss: 51.5842   val Loss: 47.4236   time: 1.94s   best: 47.4236\n",
      "Epoch 141/1000:  train Loss: 51.4232   val Loss: 47.2468   time: 1.95s   best: 47.2468\n",
      "Epoch 142/1000:  train Loss: 51.3966   val Loss: 47.2255   time: 1.95s   best: 47.2255\n",
      "Epoch 143/1000:  train Loss: 51.2230   val Loss: 46.9067   time: 1.95s   best: 46.9067\n",
      "Epoch 144/1000:  train Loss: 51.1477   val Loss: 46.9243   time: 1.95s   best: 46.9067\n",
      "Epoch 145/1000:  train Loss: 51.0079   val Loss: 46.9910   time: 1.94s   best: 46.9067\n",
      "Epoch 146/1000:  train Loss: 50.8360   val Loss: 46.5692   time: 1.93s   best: 46.5692\n",
      "Epoch 147/1000:  train Loss: 50.7244   val Loss: 46.6556   time: 1.92s   best: 46.5692\n",
      "Epoch 148/1000:  train Loss: 50.6191   val Loss: 46.5962   time: 1.91s   best: 46.5692\n",
      "Epoch 149/1000:  train Loss: 50.6000   val Loss: 46.4051   time: 1.91s   best: 46.4051\n",
      "Epoch 150/1000:  train Loss: 50.5442   val Loss: 46.4278   time: 1.91s   best: 46.4051\n",
      "Epoch 151/1000:  train Loss: 50.3686   val Loss: 46.2414   time: 1.91s   best: 46.2414\n",
      "Epoch 152/1000:  train Loss: 50.2482   val Loss: 46.1018   time: 1.94s   best: 46.1018\n",
      "Epoch 153/1000:  train Loss: 50.1801   val Loss: 45.9249   time: 1.94s   best: 45.9249\n",
      "Epoch 154/1000:  train Loss: 50.0261   val Loss: 45.9114   time: 1.95s   best: 45.9114\n",
      "Epoch 155/1000:  train Loss: 50.0534   val Loss: 45.7910   time: 1.95s   best: 45.7910\n",
      "Epoch 156/1000:  train Loss: 49.9277   val Loss: 45.7926   time: 1.95s   best: 45.7910\n",
      "Epoch 157/1000:  train Loss: 49.7605   val Loss: 45.8745   time: 1.95s   best: 45.7910\n",
      "Epoch 158/1000:  train Loss: 49.8913   val Loss: 45.6861   time: 1.93s   best: 45.6861\n",
      "Epoch 159/1000:  train Loss: 49.6312   val Loss: 45.5475   time: 1.93s   best: 45.5475\n",
      "Epoch 160/1000:  train Loss: 49.4849   val Loss: 45.4081   time: 1.92s   best: 45.4081\n",
      "Epoch 161/1000:  train Loss: 49.4480   val Loss: 45.2889   time: 1.91s   best: 45.2889\n",
      "Epoch 162/1000:  train Loss: 49.4302   val Loss: 45.3704   time: 1.91s   best: 45.2889\n",
      "Epoch 163/1000:  train Loss: 49.4014   val Loss: 45.1295   time: 1.91s   best: 45.1295\n",
      "Epoch 164/1000:  train Loss: 49.2229   val Loss: 44.9804   time: 1.91s   best: 44.9804\n",
      "Epoch 165/1000:  train Loss: 49.1127   val Loss: 45.1196   time: 1.94s   best: 44.9804\n",
      "Epoch 166/1000:  train Loss: 49.1546   val Loss: 45.0009   time: 1.94s   best: 44.9804\n",
      "Epoch 167/1000:  train Loss: 48.9077   val Loss: 44.8263   time: 1.95s   best: 44.8263\n",
      "Epoch 168/1000:  train Loss: 48.8181   val Loss: 44.9041   time: 1.94s   best: 44.8263\n",
      "Epoch 169/1000:  train Loss: 48.7597   val Loss: 44.5999   time: 1.95s   best: 44.5999\n",
      "Epoch 170/1000:  train Loss: 48.8077   val Loss: 44.7227   time: 1.95s   best: 44.5999\n",
      "Epoch 171/1000:  train Loss: 48.6476   val Loss: 44.4915   time: 1.94s   best: 44.4915\n",
      "Epoch 172/1000:  train Loss: 48.6469   val Loss: 44.6524   time: 1.93s   best: 44.4915\n",
      "Epoch 173/1000:  train Loss: 48.5135   val Loss: 44.4443   time: 1.92s   best: 44.4443\n",
      "Epoch 174/1000:  train Loss: 48.3843   val Loss: 44.3752   time: 1.91s   best: 44.3752\n",
      "Epoch 175/1000:  train Loss: 48.4697   val Loss: 44.3602   time: 1.91s   best: 44.3602\n",
      "Epoch 176/1000:  train Loss: 48.3340   val Loss: 44.2000   time: 1.91s   best: 44.2000\n",
      "Epoch 177/1000:  train Loss: 48.1801   val Loss: 43.9488   time: 1.91s   best: 43.9488\n",
      "Epoch 178/1000:  train Loss: 48.1453   val Loss: 44.0679   time: 1.93s   best: 43.9488\n",
      "Epoch 179/1000:  train Loss: 48.0257   val Loss: 43.8279   time: 1.94s   best: 43.8279\n",
      "Epoch 180/1000:  train Loss: 47.9761   val Loss: 43.8691   time: 1.95s   best: 43.8279\n",
      "Epoch 181/1000:  train Loss: 47.8566   val Loss: 43.7285   time: 1.95s   best: 43.7285\n",
      "Epoch 182/1000:  train Loss: 47.7437   val Loss: 43.7851   time: 1.95s   best: 43.7285\n",
      "Epoch 183/1000:  train Loss: 47.7879   val Loss: 43.6768   time: 1.95s   best: 43.6768\n",
      "Epoch 184/1000:  train Loss: 47.6708   val Loss: 43.6223   time: 1.94s   best: 43.6223\n",
      "Epoch 185/1000:  train Loss: 47.7992   val Loss: 43.2636   time: 1.93s   best: 43.2636\n",
      "Epoch 186/1000:  train Loss: 47.6383   val Loss: 43.3664   time: 1.92s   best: 43.2636\n",
      "Epoch 187/1000:  train Loss: 47.6495   val Loss: 43.3123   time: 1.92s   best: 43.2636\n",
      "Epoch 188/1000:  train Loss: 47.3763   val Loss: 43.3018   time: 1.91s   best: 43.2636\n",
      "Epoch 189/1000:  train Loss: 47.3708   val Loss: 43.2357   time: 1.91s   best: 43.2357\n",
      "Epoch 190/1000:  train Loss: 47.4110   val Loss: 43.0313   time: 1.91s   best: 43.0313\n",
      "Epoch 191/1000:  train Loss: 47.2412   val Loss: 43.1763   time: 1.94s   best: 43.0313\n",
      "Epoch 192/1000:  train Loss: 47.1126   val Loss: 43.0227   time: 1.95s   best: 43.0227\n",
      "Epoch 193/1000:  train Loss: 47.0842   val Loss: 42.9974   time: 1.95s   best: 42.9974\n",
      "Epoch 194/1000:  train Loss: 47.1308   val Loss: 42.8460   time: 1.95s   best: 42.8460\n",
      "Epoch 195/1000:  train Loss: 47.0071   val Loss: 43.0787   time: 1.95s   best: 42.8460\n",
      "Epoch 196/1000:  train Loss: 46.8839   val Loss: 42.7977   time: 1.95s   best: 42.7977\n",
      "Epoch 197/1000:  train Loss: 46.7915   val Loss: 42.6887   time: 1.94s   best: 42.6887\n",
      "Epoch 198/1000:  train Loss: 46.8602   val Loss: 42.6400   time: 1.92s   best: 42.6400\n",
      "Epoch 199/1000:  train Loss: 46.7433   val Loss: 42.5952   time: 1.92s   best: 42.5952\n",
      "Epoch 200/1000:  train Loss: 46.5917   val Loss: 42.4245   time: 1.91s   best: 42.4245\n",
      "Epoch 201/1000:  train Loss: 46.5633   val Loss: 42.5507   time: 1.91s   best: 42.4245\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202/1000:  train Loss: 46.5019   val Loss: 42.4004   time: 1.91s   best: 42.4004\n",
      "Epoch 203/1000:  train Loss: 46.4663   val Loss: 42.2832   time: 1.92s   best: 42.2832\n",
      "Epoch 204/1000:  train Loss: 46.3839   val Loss: 42.4237   time: 1.94s   best: 42.2832\n",
      "Epoch 205/1000:  train Loss: 46.4970   val Loss: 42.2234   time: 1.95s   best: 42.2234\n",
      "Epoch 206/1000:  train Loss: 46.3586   val Loss: 42.0166   time: 1.96s   best: 42.0166\n",
      "Epoch 207/1000:  train Loss: 46.3357   val Loss: 42.0721   time: 1.95s   best: 42.0166\n",
      "Epoch 208/1000:  train Loss: 46.2751   val Loss: 42.1738   time: 1.95s   best: 42.0166\n",
      "Epoch 209/1000:  train Loss: 46.1262   val Loss: 42.0460   time: 1.95s   best: 42.0166\n",
      "Epoch 210/1000:  train Loss: 46.0531   val Loss: 41.9556   time: 1.92s   best: 41.9556\n",
      "Epoch 211/1000:  train Loss: 46.1359   val Loss: 41.8277   time: 1.93s   best: 41.8277\n",
      "Epoch 212/1000:  train Loss: 45.9098   val Loss: 41.6275   time: 1.92s   best: 41.6275\n",
      "Epoch 213/1000:  train Loss: 45.9354   val Loss: 41.7436   time: 1.91s   best: 41.6275\n",
      "Epoch 214/1000:  train Loss: 45.7015   val Loss: 41.4666   time: 1.91s   best: 41.4666\n",
      "Epoch 215/1000:  train Loss: 45.7005   val Loss: 41.6657   time: 1.91s   best: 41.4666\n",
      "Epoch 216/1000:  train Loss: 45.7452   val Loss: 41.6190   time: 1.91s   best: 41.4666\n",
      "Epoch 217/1000:  train Loss: 45.5842   val Loss: 41.5072   time: 1.93s   best: 41.4666\n",
      "Epoch 218/1000:  train Loss: 45.6706   val Loss: 41.4854   time: 1.95s   best: 41.4666\n",
      "Epoch 219/1000:  train Loss: 45.4586   val Loss: 41.6003   time: 1.96s   best: 41.4666\n",
      "Epoch 220/1000:  train Loss: 45.5323   val Loss: 41.3844   time: 1.95s   best: 41.3844\n",
      "Epoch 221/1000:  train Loss: 45.3786   val Loss: 41.3057   time: 1.94s   best: 41.3057\n",
      "Epoch 222/1000:  train Loss: 45.4503   val Loss: 41.1686   time: 1.94s   best: 41.1686\n",
      "Epoch 223/1000:  train Loss: 45.3349   val Loss: 41.1688   time: 1.93s   best: 41.1686\n",
      "Epoch 224/1000:  train Loss: 45.2549   val Loss: 41.2716   time: 1.92s   best: 41.1686\n",
      "Epoch 225/1000:  train Loss: 45.2436   val Loss: 41.0385   time: 1.91s   best: 41.0385\n",
      "Epoch 226/1000:  train Loss: 45.1205   val Loss: 41.0241   time: 1.91s   best: 41.0241\n",
      "Epoch 227/1000:  train Loss: 45.0293   val Loss: 41.1137   time: 1.90s   best: 41.0241\n",
      "Epoch 228/1000:  train Loss: 45.1440   val Loss: 41.0508   time: 1.90s   best: 41.0241\n",
      "Epoch 229/1000:  train Loss: 45.0325   val Loss: 40.9384   time: 1.91s   best: 40.9384\n",
      "Epoch 230/1000:  train Loss: 44.9011   val Loss: 40.8863   time: 1.93s   best: 40.8863\n",
      "Epoch 231/1000:  train Loss: 44.8712   val Loss: 40.7926   time: 1.94s   best: 40.7926\n",
      "Epoch 232/1000:  train Loss: 44.8134   val Loss: 40.8627   time: 1.94s   best: 40.7926\n",
      "Epoch 233/1000:  train Loss: 44.7515   val Loss: 40.8024   time: 1.95s   best: 40.7926\n",
      "Epoch 234/1000:  train Loss: 44.7186   val Loss: 40.7108   time: 1.94s   best: 40.7108\n",
      "Epoch 235/1000:  train Loss: 44.6632   val Loss: 40.5749   time: 1.94s   best: 40.5749\n",
      "Epoch 236/1000:  train Loss: 44.6435   val Loss: 40.5664   time: 1.93s   best: 40.5664\n",
      "Epoch 237/1000:  train Loss: 44.5990   val Loss: 40.5811   time: 1.92s   best: 40.5664\n",
      "Epoch 238/1000:  train Loss: 44.5498   val Loss: 40.3393   time: 1.91s   best: 40.3393\n",
      "Epoch 239/1000:  train Loss: 44.5687   val Loss: 40.5137   time: 1.91s   best: 40.3393\n",
      "Epoch 240/1000:  train Loss: 44.4334   val Loss: 40.5920   time: 1.90s   best: 40.3393\n",
      "Epoch 241/1000:  train Loss: 44.4165   val Loss: 40.5311   time: 1.90s   best: 40.3393\n",
      "Epoch 242/1000:  train Loss: 44.3748   val Loss: 40.2231   time: 1.91s   best: 40.2231\n",
      "Epoch 243/1000:  train Loss: 44.2927   val Loss: 40.3818   time: 1.93s   best: 40.2231\n",
      "Epoch 244/1000:  train Loss: 44.2543   val Loss: 40.3345   time: 1.94s   best: 40.2231\n",
      "Epoch 245/1000:  train Loss: 44.2705   val Loss: 40.3199   time: 1.95s   best: 40.2231\n",
      "Epoch 246/1000:  train Loss: 44.1131   val Loss: 40.3867   time: 1.94s   best: 40.2231\n",
      "Epoch 247/1000:  train Loss: 44.2838   val Loss: 40.1030   time: 1.94s   best: 40.1030\n",
      "Epoch 248/1000:  train Loss: 44.0725   val Loss: 40.0834   time: 1.94s   best: 40.0834\n",
      "Epoch 249/1000:  train Loss: 44.0583   val Loss: 40.0764   time: 1.92s   best: 40.0764\n",
      "Epoch 250/1000:  train Loss: 43.9606   val Loss: 40.2798   time: 1.92s   best: 40.0764\n",
      "Epoch 251/1000:  train Loss: 43.9342   val Loss: 39.9651   time: 1.91s   best: 39.9651\n",
      "Epoch 252/1000:  train Loss: 43.8218   val Loss: 40.1515   time: 1.91s   best: 39.9651\n",
      "Epoch 253/1000:  train Loss: 43.8765   val Loss: 39.8573   time: 1.91s   best: 39.8573\n",
      "Epoch 254/1000:  train Loss: 43.8852   val Loss: 39.8703   time: 1.90s   best: 39.8573\n",
      "Epoch 255/1000:  train Loss: 43.8300   val Loss: 39.9233   time: 1.91s   best: 39.8573\n",
      "Epoch 256/1000:  train Loss: 43.6701   val Loss: 39.8724   time: 1.93s   best: 39.8573\n",
      "Epoch 257/1000:  train Loss: 43.6658   val Loss: 39.8114   time: 1.94s   best: 39.8114\n",
      "Epoch 258/1000:  train Loss: 43.7005   val Loss: 39.6855   time: 1.94s   best: 39.6855\n",
      "Epoch 259/1000:  train Loss: 43.5838   val Loss: 39.7264   time: 1.95s   best: 39.6855\n",
      "Epoch 260/1000:  train Loss: 43.4912   val Loss: 39.7957   time: 1.94s   best: 39.6855\n",
      "Epoch 261/1000:  train Loss: 43.5628   val Loss: 39.5726   time: 1.94s   best: 39.5726\n",
      "Epoch 262/1000:  train Loss: 43.5494   val Loss: 39.7010   time: 1.93s   best: 39.5726\n",
      "Epoch 263/1000:  train Loss: 43.4212   val Loss: 39.5014   time: 1.92s   best: 39.5014\n",
      "Epoch 264/1000:  train Loss: 43.2978   val Loss: 39.5817   time: 1.91s   best: 39.5014\n",
      "Epoch 265/1000:  train Loss: 43.3894   val Loss: 39.6371   time: 1.91s   best: 39.5014\n",
      "Epoch 266/1000:  train Loss: 43.3291   val Loss: 39.5177   time: 1.90s   best: 39.5014\n",
      "Epoch 267/1000:  train Loss: 43.2752   val Loss: 39.3639   time: 1.90s   best: 39.3639\n",
      "Epoch 268/1000:  train Loss: 43.0925   val Loss: 39.2998   time: 1.90s   best: 39.2998\n",
      "Epoch 269/1000:  train Loss: 43.3149   val Loss: 39.4518   time: 1.93s   best: 39.2998\n",
      "Epoch 270/1000:  train Loss: 43.0995   val Loss: 39.2884   time: 1.94s   best: 39.2884\n",
      "Epoch 271/1000:  train Loss: 43.0663   val Loss: 39.2453   time: 1.95s   best: 39.2453\n",
      "Epoch 272/1000:  train Loss: 43.0336   val Loss: 39.2322   time: 1.94s   best: 39.2322\n",
      "Epoch 273/1000:  train Loss: 43.0763   val Loss: 39.4386   time: 1.94s   best: 39.2322\n",
      "Epoch 274/1000:  train Loss: 43.0001   val Loss: 39.1874   time: 1.94s   best: 39.1874\n",
      "Epoch 275/1000:  train Loss: 42.9499   val Loss: 39.1099   time: 1.93s   best: 39.1099\n",
      "Epoch 276/1000:  train Loss: 42.9137   val Loss: 39.0284   time: 1.91s   best: 39.0284\n",
      "Epoch 277/1000:  train Loss: 42.8284   val Loss: 39.0606   time: 1.91s   best: 39.0284\n",
      "Epoch 278/1000:  train Loss: 42.7836   val Loss: 39.1973   time: 1.90s   best: 39.0284\n",
      "Epoch 279/1000:  train Loss: 42.9326   val Loss: 38.9637   time: 1.90s   best: 38.9637\n",
      "Epoch 280/1000:  train Loss: 42.7611   val Loss: 39.0139   time: 1.90s   best: 38.9637\n",
      "Epoch 281/1000:  train Loss: 42.6298   val Loss: 39.0135   time: 1.90s   best: 38.9637\n",
      "Epoch 282/1000:  train Loss: 42.7873   val Loss: 38.9077   time: 1.91s   best: 38.9077\n",
      "Epoch 283/1000:  train Loss: 42.6470   val Loss: 39.1385   time: 1.93s   best: 38.9077\n",
      "Epoch 284/1000:  train Loss: 42.6734   val Loss: 38.9633   time: 1.94s   best: 38.9077\n",
      "Epoch 285/1000:  train Loss: 42.6970   val Loss: 38.7788   time: 1.94s   best: 38.7788\n",
      "Epoch 286/1000:  train Loss: 42.5802   val Loss: 38.7241   time: 1.94s   best: 38.7241\n",
      "Epoch 287/1000:  train Loss: 42.4938   val Loss: 38.8047   time: 1.94s   best: 38.7241\n",
      "Epoch 288/1000:  train Loss: 42.4596   val Loss: 38.8785   time: 1.93s   best: 38.7241\n",
      "Epoch 289/1000:  train Loss: 42.4461   val Loss: 38.7114   time: 1.92s   best: 38.7114\n",
      "Epoch 290/1000:  train Loss: 42.4016   val Loss: 38.6980   time: 1.91s   best: 38.6980\n",
      "Epoch 291/1000:  train Loss: 42.4509   val Loss: 38.8782   time: 1.90s   best: 38.6980\n",
      "Epoch 292/1000:  train Loss: 42.2795   val Loss: 38.6943   time: 1.90s   best: 38.6943\n",
      "Epoch 293/1000:  train Loss: 42.2894   val Loss: 38.6252   time: 1.90s   best: 38.6252\n",
      "Epoch 294/1000:  train Loss: 42.2648   val Loss: 38.6152   time: 1.90s   best: 38.6152\n",
      "Epoch 295/1000:  train Loss: 42.1013   val Loss: 38.5814   time: 1.93s   best: 38.5814\n",
      "Epoch 296/1000:  train Loss: 42.2256   val Loss: 38.6121   time: 1.94s   best: 38.5814\n",
      "Epoch 297/1000:  train Loss: 42.1646   val Loss: 38.5466   time: 1.95s   best: 38.5466\n",
      "Epoch 298/1000:  train Loss: 42.1299   val Loss: 38.4882   time: 1.94s   best: 38.4882\n",
      "Epoch 299/1000:  train Loss: 42.0646   val Loss: 38.5262   time: 1.95s   best: 38.4882\n",
      "Epoch 300/1000:  train Loss: 42.1319   val Loss: 38.4351   time: 1.94s   best: 38.4351\n",
      "Epoch 301/1000:  train Loss: 42.1450   val Loss: 38.4184   time: 1.94s   best: 38.4184\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 302/1000:  train Loss: 42.0148   val Loss: 38.3691   time: 1.92s   best: 38.3691\n",
      "Epoch 303/1000:  train Loss: 41.9668   val Loss: 38.3888   time: 1.91s   best: 38.3691\n",
      "Epoch 304/1000:  train Loss: 42.0083   val Loss: 38.3382   time: 1.91s   best: 38.3382\n",
      "Epoch 305/1000:  train Loss: 41.8123   val Loss: 38.1983   time: 1.90s   best: 38.1983\n",
      "Epoch 306/1000:  train Loss: 41.9055   val Loss: 38.2017   time: 1.90s   best: 38.1983\n",
      "Epoch 307/1000:  train Loss: 41.7738   val Loss: 38.2098   time: 1.92s   best: 38.1983\n",
      "Epoch 308/1000:  train Loss: 41.8244   val Loss: 38.2164   time: 1.93s   best: 38.1983\n",
      "Epoch 309/1000:  train Loss: 41.7524   val Loss: 38.4303   time: 1.94s   best: 38.1983\n",
      "Epoch 310/1000:  train Loss: 41.6644   val Loss: 38.2074   time: 1.94s   best: 38.1983\n",
      "Epoch 311/1000:  train Loss: 41.7161   val Loss: 38.0789   time: 1.95s   best: 38.0789\n",
      "Epoch 312/1000:  train Loss: 41.7227   val Loss: 38.1702   time: 1.94s   best: 38.0789\n",
      "Epoch 313/1000:  train Loss: 41.5249   val Loss: 38.0589   time: 1.94s   best: 38.0589\n",
      "Epoch 314/1000:  train Loss: 41.7021   val Loss: 38.2241   time: 1.93s   best: 38.0589\n",
      "Epoch 315/1000:  train Loss: 41.5915   val Loss: 37.9995   time: 1.92s   best: 37.9995\n",
      "Epoch 316/1000:  train Loss: 41.5423   val Loss: 37.9972   time: 1.91s   best: 37.9972\n",
      "Epoch 317/1000:  train Loss: 41.5045   val Loss: 37.8251   time: 1.90s   best: 37.8251\n",
      "Epoch 318/1000:  train Loss: 41.5236   val Loss: 37.9565   time: 1.90s   best: 37.8251\n",
      "Epoch 319/1000:  train Loss: 41.5145   val Loss: 37.8597   time: 1.90s   best: 37.8251\n",
      "Epoch 320/1000:  train Loss: 41.3608   val Loss: 37.8206   time: 1.92s   best: 37.8206\n",
      "Epoch 321/1000:  train Loss: 41.3333   val Loss: 37.8605   time: 1.93s   best: 37.8206\n",
      "Epoch 322/1000:  train Loss: 41.4548   val Loss: 37.9924   time: 1.94s   best: 37.8206\n",
      "Epoch 323/1000:  train Loss: 41.2811   val Loss: 37.9230   time: 1.94s   best: 37.8206\n",
      "Epoch 324/1000:  train Loss: 41.3555   val Loss: 37.8994   time: 1.94s   best: 37.8206\n",
      "Epoch 325/1000:  train Loss: 41.2424   val Loss: 37.8084   time: 1.94s   best: 37.8084\n",
      "Epoch 326/1000:  train Loss: 41.1440   val Loss: 37.9452   time: 1.93s   best: 37.8084\n",
      "Epoch 327/1000:  train Loss: 41.3334   val Loss: 37.7367   time: 1.92s   best: 37.7367\n",
      "Epoch 328/1000:  train Loss: 41.1321   val Loss: 37.7996   time: 1.91s   best: 37.7367\n",
      "Epoch 329/1000:  train Loss: 41.2684   val Loss: 37.8325   time: 1.91s   best: 37.7367\n",
      "Epoch 330/1000:  train Loss: 41.1474   val Loss: 37.7649   time: 1.90s   best: 37.7367\n",
      "Epoch 331/1000:  train Loss: 41.2275   val Loss: 37.8024   time: 1.90s   best: 37.7367\n",
      "Epoch 332/1000:  train Loss: 41.1108   val Loss: 37.7292   time: 1.90s   best: 37.7292\n",
      "Epoch 333/1000:  train Loss: 41.0651   val Loss: 37.8025   time: 1.93s   best: 37.7292\n",
      "Epoch 334/1000:  train Loss: 41.1864   val Loss: 37.5818   time: 1.93s   best: 37.5818\n",
      "Epoch 335/1000:  train Loss: 40.9812   val Loss: 37.3977   time: 1.94s   best: 37.3977\n",
      "Epoch 336/1000:  train Loss: 40.8924   val Loss: 37.6376   time: 1.94s   best: 37.3977\n",
      "Epoch 337/1000:  train Loss: 40.9400   val Loss: 37.5749   time: 1.94s   best: 37.3977\n",
      "Epoch 338/1000:  train Loss: 40.9792   val Loss: 37.5504   time: 1.94s   best: 37.3977\n",
      "Epoch 339/1000:  train Loss: 40.9661   val Loss: 37.5672   time: 1.93s   best: 37.3977\n",
      "Epoch 340/1000:  train Loss: 40.7734   val Loss: 37.5027   time: 1.92s   best: 37.3977\n",
      "Epoch 341/1000:  train Loss: 40.8094   val Loss: 37.4916   time: 1.92s   best: 37.3977\n",
      "Epoch 342/1000:  train Loss: 40.8455   val Loss: 37.4831   time: 1.91s   best: 37.3977\n",
      "Epoch 343/1000:  train Loss: 40.8166   val Loss: 37.3422   time: 1.90s   best: 37.3422\n",
      "Epoch 344/1000:  train Loss: 40.8480   val Loss: 37.6027   time: 1.90s   best: 37.3422\n",
      "Epoch 345/1000:  train Loss: 40.8275   val Loss: 37.4176   time: 1.90s   best: 37.3422\n",
      "Epoch 346/1000:  train Loss: 40.6703   val Loss: 37.3804   time: 1.92s   best: 37.3422\n",
      "Epoch 347/1000:  train Loss: 40.7065   val Loss: 37.2948   time: 1.93s   best: 37.2948\n",
      "Epoch 348/1000:  train Loss: 40.6406   val Loss: 37.3007   time: 1.95s   best: 37.2948\n",
      "Epoch 349/1000:  train Loss: 40.5992   val Loss: 37.3480   time: 1.95s   best: 37.2948\n",
      "Epoch 350/1000:  train Loss: 40.6230   val Loss: 37.2519   time: 1.94s   best: 37.2519\n",
      "Epoch 351/1000:  train Loss: 40.5807   val Loss: 37.3012   time: 1.94s   best: 37.2519\n",
      "Epoch 352/1000:  train Loss: 40.5876   val Loss: 37.2682   time: 1.93s   best: 37.2519\n",
      "Epoch 353/1000:  train Loss: 40.5477   val Loss: 37.1833   time: 1.93s   best: 37.1833\n",
      "Epoch 354/1000:  train Loss: 40.5402   val Loss: 37.2963   time: 1.91s   best: 37.1833\n",
      "Epoch 355/1000:  train Loss: 40.4752   val Loss: 37.3020   time: 1.91s   best: 37.1833\n",
      "Epoch 356/1000:  train Loss: 40.3651   val Loss: 37.1432   time: 1.90s   best: 37.1432\n",
      "Epoch 357/1000:  train Loss: 40.4399   val Loss: 37.4369   time: 1.90s   best: 37.1432\n",
      "Epoch 358/1000:  train Loss: 40.3860   val Loss: 37.0366   time: 1.91s   best: 37.0366\n",
      "Epoch 359/1000:  train Loss: 40.4022   val Loss: 37.2299   time: 1.93s   best: 37.0366\n",
      "Epoch 360/1000:  train Loss: 40.4143   val Loss: 37.1997   time: 1.94s   best: 37.0366\n",
      "Epoch 361/1000:  train Loss: 40.3110   val Loss: 37.1150   time: 1.95s   best: 37.0366\n",
      "Epoch 362/1000:  train Loss: 40.4493   val Loss: 37.1560   time: 1.94s   best: 37.0366\n",
      "Epoch 363/1000:  train Loss: 40.2810   val Loss: 37.0724   time: 1.94s   best: 37.0366\n",
      "Epoch 364/1000:  train Loss: 40.3380   val Loss: 37.0018   time: 1.94s   best: 37.0018\n",
      "Epoch 365/1000:  train Loss: 40.1739   val Loss: 37.0493   time: 1.93s   best: 37.0018\n",
      "Epoch 366/1000:  train Loss: 40.1820   val Loss: 37.0201   time: 1.91s   best: 37.0018\n",
      "Epoch 367/1000:  train Loss: 40.1739   val Loss: 36.8141   time: 1.92s   best: 36.8141\n",
      "Epoch 368/1000:  train Loss: 40.1176   val Loss: 36.8484   time: 1.91s   best: 36.8141\n",
      "Epoch 369/1000:  train Loss: 40.0789   val Loss: 37.0739   time: 1.90s   best: 36.8141\n",
      "Epoch 370/1000:  train Loss: 40.1555   val Loss: 36.9126   time: 1.90s   best: 36.8141\n",
      "Epoch 371/1000:  train Loss: 40.1730   val Loss: 36.8932   time: 1.91s   best: 36.8141\n",
      "Epoch 372/1000:  train Loss: 40.0813   val Loss: 36.7610   time: 1.92s   best: 36.7610\n",
      "Epoch 373/1000:  train Loss: 40.0765   val Loss: 37.1712   time: 1.94s   best: 36.7610\n",
      "Epoch 374/1000:  train Loss: 39.9722   val Loss: 36.8602   time: 1.95s   best: 36.7610\n",
      "Epoch 375/1000:  train Loss: 40.0745   val Loss: 36.9899   time: 1.95s   best: 36.7610\n",
      "Epoch 376/1000:  train Loss: 40.0531   val Loss: 36.6670   time: 1.94s   best: 36.6670\n",
      "Epoch 377/1000:  train Loss: 39.8876   val Loss: 36.6895   time: 1.94s   best: 36.6670\n",
      "Epoch 378/1000:  train Loss: 39.9420   val Loss: 36.7892   time: 1.93s   best: 36.6670\n",
      "Epoch 379/1000:  train Loss: 39.9667   val Loss: 36.8168   time: 1.93s   best: 36.6670\n",
      "Epoch 380/1000:  train Loss: 39.8688   val Loss: 36.7333   time: 1.92s   best: 36.6670\n",
      "Epoch 381/1000:  train Loss: 39.8994   val Loss: 36.7913   time: 1.91s   best: 36.6670\n",
      "Epoch 382/1000:  train Loss: 39.9480   val Loss: 36.8064   time: 1.91s   best: 36.6670\n",
      "Epoch 383/1000:  train Loss: 39.8006   val Loss: 36.8530   time: 1.90s   best: 36.6670\n",
      "Epoch 384/1000:  train Loss: 39.8798   val Loss: 36.6515   time: 1.91s   best: 36.6515\n",
      "Epoch 385/1000:  train Loss: 39.6697   val Loss: 36.7179   time: 1.93s   best: 36.6515\n",
      "Epoch 386/1000:  train Loss: 39.7629   val Loss: 36.5970   time: 1.94s   best: 36.5970\n",
      "Epoch 387/1000:  train Loss: 39.7823   val Loss: 36.5256   time: 1.95s   best: 36.5256\n",
      "Epoch 388/1000:  train Loss: 39.6932   val Loss: 36.6320   time: 1.95s   best: 36.5256\n",
      "Epoch 389/1000:  train Loss: 39.7591   val Loss: 36.5611   time: 1.95s   best: 36.5256\n",
      "Epoch 390/1000:  train Loss: 39.5666   val Loss: 36.4221   time: 1.94s   best: 36.4221\n",
      "Epoch 391/1000:  train Loss: 39.6714   val Loss: 36.3870   time: 1.94s   best: 36.3870\n",
      "Epoch 392/1000:  train Loss: 39.7025   val Loss: 36.5860   time: 1.92s   best: 36.3870\n",
      "Epoch 393/1000:  train Loss: 39.6014   val Loss: 36.6340   time: 1.91s   best: 36.3870\n",
      "Epoch 394/1000:  train Loss: 39.5147   val Loss: 36.4491   time: 1.91s   best: 36.3870\n",
      "Epoch 395/1000:  train Loss: 39.5439   val Loss: 36.4969   time: 1.91s   best: 36.3870\n",
      "Epoch 396/1000:  train Loss: 39.6659   val Loss: 36.5036   time: 1.90s   best: 36.3870\n",
      "Epoch 397/1000:  train Loss: 39.5487   val Loss: 36.8543   time: 1.92s   best: 36.3870\n",
      "Epoch 398/1000:  train Loss: 39.5741   val Loss: 36.4220   time: 1.94s   best: 36.3870\n",
      "Epoch 399/1000:  train Loss: 39.4129   val Loss: 36.4147   time: 1.94s   best: 36.3870\n",
      "Epoch 400/1000:  train Loss: 39.5361   val Loss: 36.3923   time: 1.95s   best: 36.3870\n",
      "Epoch 401/1000:  train Loss: 39.4991   val Loss: 36.4238   time: 1.95s   best: 36.3870\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 402/1000:  train Loss: 39.3792   val Loss: 36.3776   time: 1.95s   best: 36.3776\n",
      "Epoch 403/1000:  train Loss: 39.4872   val Loss: 36.3977   time: 1.95s   best: 36.3776\n",
      "Epoch 404/1000:  train Loss: 39.4154   val Loss: 36.2098   time: 1.93s   best: 36.2098\n",
      "Epoch 405/1000:  train Loss: 39.2695   val Loss: 36.2006   time: 1.92s   best: 36.2006\n",
      "Epoch 406/1000:  train Loss: 39.4134   val Loss: 36.2731   time: 1.91s   best: 36.2006\n",
      "Epoch 407/1000:  train Loss: 39.2595   val Loss: 36.4799   time: 1.91s   best: 36.2006\n",
      "Epoch 408/1000:  train Loss: 39.4662   val Loss: 36.3119   time: 1.90s   best: 36.2006\n",
      "Epoch 409/1000:  train Loss: 39.2200   val Loss: 36.3887   time: 1.91s   best: 36.2006\n",
      "Epoch 410/1000:  train Loss: 39.1217   val Loss: 36.2533   time: 1.93s   best: 36.2006\n",
      "Epoch 411/1000:  train Loss: 39.1546   val Loss: 36.0847   time: 1.94s   best: 36.0847\n",
      "Epoch 412/1000:  train Loss: 39.1938   val Loss: 36.3059   time: 1.95s   best: 36.0847\n",
      "Epoch 413/1000:  train Loss: 39.1763   val Loss: 36.1162   time: 1.95s   best: 36.0847\n",
      "Epoch 414/1000:  train Loss: 39.1853   val Loss: 36.3521   time: 1.95s   best: 36.0847\n",
      "Epoch 415/1000:  train Loss: 39.1839   val Loss: 36.2629   time: 1.95s   best: 36.0847\n",
      "Epoch 416/1000:  train Loss: 39.1895   val Loss: 36.2526   time: 1.93s   best: 36.0847\n",
      "Epoch 417/1000:  train Loss: 39.1432   val Loss: 36.2678   time: 1.93s   best: 36.0847\n",
      "Epoch 418/1000:  train Loss: 39.1076   val Loss: 36.2014   time: 1.93s   best: 36.0847\n",
      "Epoch 419/1000:  train Loss: 39.0872   val Loss: 36.3222   time: 1.92s   best: 36.0847\n",
      "Epoch 420/1000:  train Loss: 39.1847   val Loss: 36.0794   time: 1.91s   best: 36.0794\n",
      "Epoch 421/1000:  train Loss: 39.0515   val Loss: 36.1183   time: 1.91s   best: 36.0794\n",
      "Epoch 422/1000:  train Loss: 38.9413   val Loss: 36.1781   time: 1.91s   best: 36.0794\n",
      "Epoch 423/1000:  train Loss: 39.0087   val Loss: 35.9735   time: 1.93s   best: 35.9735\n",
      "Epoch 424/1000:  train Loss: 39.0588   val Loss: 36.1577   time: 1.94s   best: 35.9735\n",
      "Epoch 425/1000:  train Loss: 38.9607   val Loss: 36.0773   time: 1.95s   best: 35.9735\n",
      "Epoch 426/1000:  train Loss: 39.0436   val Loss: 36.0651   time: 1.95s   best: 35.9735\n",
      "Epoch 427/1000:  train Loss: 38.9318   val Loss: 36.2020   time: 1.95s   best: 35.9735\n",
      "Epoch 428/1000:  train Loss: 38.9459   val Loss: 36.0291   time: 1.94s   best: 35.9735\n",
      "Epoch 429/1000:  train Loss: 38.9475   val Loss: 35.9947   time: 1.93s   best: 35.9735\n",
      "Epoch 430/1000:  train Loss: 38.8102   val Loss: 36.0109   time: 1.92s   best: 35.9735\n",
      "Epoch 431/1000:  train Loss: 38.8862   val Loss: 36.0133   time: 1.91s   best: 35.9735\n",
      "Epoch 432/1000:  train Loss: 38.8109   val Loss: 35.9634   time: 1.90s   best: 35.9634\n",
      "Epoch 433/1000:  train Loss: 38.7809   val Loss: 36.0616   time: 1.90s   best: 35.9634\n",
      "Epoch 434/1000:  train Loss: 38.8888   val Loss: 36.0185   time: 1.90s   best: 35.9634\n",
      "Epoch 435/1000:  train Loss: 38.8257   val Loss: 36.0632   time: 1.90s   best: 35.9634\n",
      "Epoch 436/1000:  train Loss: 38.9030   val Loss: 35.8412   time: 1.92s   best: 35.8412\n",
      "Epoch 437/1000:  train Loss: 38.8354   val Loss: 36.0239   time: 1.93s   best: 35.8412\n",
      "Epoch 438/1000:  train Loss: 38.8626   val Loss: 35.9504   time: 1.94s   best: 35.8412\n",
      "Epoch 439/1000:  train Loss: 38.6930   val Loss: 35.9136   time: 1.95s   best: 35.8412\n",
      "Epoch 440/1000:  train Loss: 38.5158   val Loss: 35.9172   time: 1.94s   best: 35.8412\n",
      "Epoch 441/1000:  train Loss: 38.6418   val Loss: 35.8410   time: 1.94s   best: 35.8410\n",
      "Epoch 442/1000:  train Loss: 38.6918   val Loss: 36.1922   time: 1.93s   best: 35.8410\n",
      "Epoch 443/1000:  train Loss: 38.7183   val Loss: 35.8575   time: 1.92s   best: 35.8410\n",
      "Epoch 444/1000:  train Loss: 38.7183   val Loss: 35.9820   time: 1.90s   best: 35.8410\n",
      "Epoch 445/1000:  train Loss: 38.5456   val Loss: 35.9206   time: 1.91s   best: 35.8410\n",
      "Epoch 446/1000:  train Loss: 38.6112   val Loss: 35.9482   time: 1.90s   best: 35.8410\n",
      "Epoch 447/1000:  train Loss: 38.5760   val Loss: 35.7416   time: 1.90s   best: 35.7416\n",
      "Epoch 448/1000:  train Loss: 38.5760   val Loss: 35.8254   time: 1.90s   best: 35.7416\n",
      "Epoch 449/1000:  train Loss: 38.4725   val Loss: 35.6662   time: 1.92s   best: 35.6662\n",
      "Epoch 450/1000:  train Loss: 38.4250   val Loss: 35.6714   time: 1.94s   best: 35.6662\n",
      "Epoch 451/1000:  train Loss: 38.5667   val Loss: 35.7046   time: 1.94s   best: 35.6662\n",
      "Epoch 452/1000:  train Loss: 38.4868   val Loss: 35.7973   time: 1.94s   best: 35.6662\n",
      "Epoch 453/1000:  train Loss: 38.5384   val Loss: 35.6179   time: 1.94s   best: 35.6179\n",
      "Epoch 454/1000:  train Loss: 38.4245   val Loss: 35.6448   time: 1.94s   best: 35.6179\n",
      "Epoch 455/1000:  train Loss: 38.3678   val Loss: 35.5954   time: 1.93s   best: 35.5954\n",
      "Epoch 456/1000:  train Loss: 38.4432   val Loss: 35.5099   time: 1.91s   best: 35.5099\n",
      "Epoch 457/1000:  train Loss: 38.3371   val Loss: 35.6933   time: 1.92s   best: 35.5099\n",
      "Epoch 458/1000:  train Loss: 38.4092   val Loss: 35.9153   time: 1.91s   best: 35.5099\n",
      "Epoch 459/1000:  train Loss: 38.4256   val Loss: 35.4882   time: 1.90s   best: 35.4882\n",
      "Epoch 460/1000:  train Loss: 38.3031   val Loss: 35.6293   time: 1.90s   best: 35.4882\n",
      "Epoch 461/1000:  train Loss: 38.3256   val Loss: 35.5078   time: 1.91s   best: 35.4882\n",
      "Epoch 462/1000:  train Loss: 38.2900   val Loss: 35.6376   time: 1.92s   best: 35.4882\n",
      "Epoch 463/1000:  train Loss: 38.3165   val Loss: 35.6434   time: 1.93s   best: 35.4882\n",
      "Epoch 464/1000:  train Loss: 38.4710   val Loss: 35.5595   time: 1.95s   best: 35.4882\n",
      "Epoch 465/1000:  train Loss: 38.2237   val Loss: 35.5931   time: 1.94s   best: 35.4882\n",
      "Epoch 466/1000:  train Loss: 38.2661   val Loss: 35.6093   time: 1.94s   best: 35.4882\n",
      "Epoch 467/1000:  train Loss: 38.3328   val Loss: 35.5071   time: 1.94s   best: 35.4882\n",
      "Epoch 468/1000:  train Loss: 38.2188   val Loss: 35.4289   time: 1.93s   best: 35.4289\n",
      "Epoch 469/1000:  train Loss: 38.2055   val Loss: 35.5779   time: 1.93s   best: 35.4289\n",
      "Epoch 470/1000:  train Loss: 38.1980   val Loss: 35.5469   time: 1.91s   best: 35.4289\n",
      "Epoch 471/1000:  train Loss: 38.1908   val Loss: 35.5195   time: 1.91s   best: 35.4289\n",
      "Epoch 472/1000:  train Loss: 38.3667   val Loss: 35.4281   time: 1.90s   best: 35.4281\n",
      "Epoch 473/1000:  train Loss: 38.0907   val Loss: 35.3558   time: 1.90s   best: 35.3558\n",
      "Epoch 474/1000:  train Loss: 38.1604   val Loss: 35.4785   time: 1.90s   best: 35.3558\n",
      "Epoch 475/1000:  train Loss: 38.0652   val Loss: 35.3761   time: 1.92s   best: 35.3558\n",
      "Epoch 476/1000:  train Loss: 38.1140   val Loss: 35.4159   time: 1.94s   best: 35.3558\n",
      "Epoch 477/1000:  train Loss: 38.1382   val Loss: 35.5184   time: 1.95s   best: 35.3558\n",
      "Epoch 478/1000:  train Loss: 38.0367   val Loss: 35.5391   time: 1.94s   best: 35.3558\n",
      "Epoch 479/1000:  train Loss: 38.0841   val Loss: 35.4996   time: 1.95s   best: 35.3558\n",
      "Epoch 480/1000:  train Loss: 38.1065   val Loss: 35.4642   time: 1.94s   best: 35.3558\n",
      "Epoch 481/1000:  train Loss: 38.0127   val Loss: 35.2889   time: 1.94s   best: 35.2889\n",
      "Epoch 482/1000:  train Loss: 38.0148   val Loss: 35.3815   time: 1.92s   best: 35.2889\n",
      "Epoch 483/1000:  train Loss: 37.9119   val Loss: 35.3517   time: 1.91s   best: 35.2889\n",
      "Epoch 484/1000:  train Loss: 37.9095   val Loss: 35.2413   time: 1.91s   best: 35.2413\n",
      "Epoch 485/1000:  train Loss: 38.0017   val Loss: 35.4796   time: 1.90s   best: 35.2413\n",
      "Epoch 486/1000:  train Loss: 37.9420   val Loss: 35.2706   time: 1.90s   best: 35.2413\n",
      "Epoch 487/1000:  train Loss: 38.0426   val Loss: 35.3513   time: 1.91s   best: 35.2413\n",
      "Epoch 488/1000:  train Loss: 37.8226   val Loss: 35.3534   time: 1.93s   best: 35.2413\n",
      "Epoch 489/1000:  train Loss: 37.9391   val Loss: 35.1765   time: 1.94s   best: 35.1765\n",
      "Epoch 490/1000:  train Loss: 37.8616   val Loss: 35.2489   time: 1.95s   best: 35.1765\n",
      "Epoch 491/1000:  train Loss: 37.8564   val Loss: 35.1911   time: 1.95s   best: 35.1765\n",
      "Epoch 492/1000:  train Loss: 37.8718   val Loss: 35.2532   time: 1.94s   best: 35.1765\n",
      "Epoch 493/1000:  train Loss: 37.7599   val Loss: 35.2548   time: 1.95s   best: 35.1765\n",
      "Epoch 494/1000:  train Loss: 37.8231   val Loss: 35.3407   time: 1.92s   best: 35.1765\n",
      "Epoch 495/1000:  train Loss: 37.8694   val Loss: 35.0973   time: 1.92s   best: 35.0973\n",
      "Epoch 496/1000:  train Loss: 37.6364   val Loss: 35.2366   time: 1.91s   best: 35.0973\n",
      "Epoch 497/1000:  train Loss: 37.7709   val Loss: 35.2441   time: 1.91s   best: 35.0973\n",
      "Epoch 498/1000:  train Loss: 37.8366   val Loss: 35.3088   time: 1.90s   best: 35.0973\n",
      "Epoch 499/1000:  train Loss: 37.7101   val Loss: 35.2624   time: 1.91s   best: 35.0973\n",
      "Epoch 500/1000:  train Loss: 37.7514   val Loss: 35.3264   time: 1.91s   best: 35.0973\n",
      "Epoch 501/1000:  train Loss: 37.6910   val Loss: 35.3931   time: 1.94s   best: 35.0973\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 502/1000:  train Loss: 37.7170   val Loss: 35.3253   time: 1.95s   best: 35.0973\n",
      "Epoch 503/1000:  train Loss: 37.7309   val Loss: 35.1811   time: 1.96s   best: 35.0973\n",
      "Epoch 504/1000:  train Loss: 37.7680   val Loss: 35.1786   time: 1.95s   best: 35.0973\n",
      "Epoch 505/1000:  train Loss: 37.6382   val Loss: 35.3948   time: 1.95s   best: 35.0973\n",
      "Epoch 506/1000:  train Loss: 37.6342   val Loss: 35.1674   time: 1.95s   best: 35.0973\n",
      "Epoch 507/1000:  train Loss: 37.6570   val Loss: 35.2259   time: 1.93s   best: 35.0973\n",
      "Epoch 508/1000:  train Loss: 37.6051   val Loss: 35.4201   time: 1.93s   best: 35.0973\n",
      "Epoch 509/1000:  train Loss: 37.7376   val Loss: 35.1381   time: 1.92s   best: 35.0973\n",
      "Epoch 510/1000:  train Loss: 37.5665   val Loss: 35.1868   time: 1.91s   best: 35.0973\n",
      "Epoch 511/1000:  train Loss: 37.5674   val Loss: 35.0873   time: 1.91s   best: 35.0873\n",
      "Epoch 512/1000:  train Loss: 37.5966   val Loss: 35.1776   time: 1.91s   best: 35.0873\n",
      "Epoch 513/1000:  train Loss: 37.5013   val Loss: 35.1388   time: 1.92s   best: 35.0873\n",
      "Epoch 514/1000:  train Loss: 37.5301   val Loss: 35.1869   time: 1.94s   best: 35.0873\n",
      "Epoch 515/1000:  train Loss: 37.5077   val Loss: 35.1811   time: 1.95s   best: 35.0873\n",
      "Epoch 516/1000:  train Loss: 37.5129   val Loss: 34.9727   time: 1.95s   best: 34.9727\n",
      "Epoch 517/1000:  train Loss: 37.4700   val Loss: 35.0019   time: 1.95s   best: 34.9727\n",
      "Epoch 518/1000:  train Loss: 37.3942   val Loss: 35.0717   time: 1.95s   best: 34.9727\n",
      "Epoch 519/1000:  train Loss: 37.3936   val Loss: 35.2361   time: 1.95s   best: 34.9727\n",
      "Epoch 520/1000:  train Loss: 37.6209   val Loss: 35.2386   time: 1.93s   best: 34.9727\n",
      "Epoch 521/1000:  train Loss: 37.4019   val Loss: 35.2493   time: 1.93s   best: 34.9727\n",
      "Epoch 522/1000:  train Loss: 37.3458   val Loss: 35.0846   time: 1.91s   best: 34.9727\n",
      "Epoch 523/1000:  train Loss: 37.3615   val Loss: 35.0453   time: 1.91s   best: 34.9727\n",
      "Epoch 524/1000:  train Loss: 37.4446   val Loss: 34.9881   time: 1.91s   best: 34.9727\n",
      "Epoch 525/1000:  train Loss: 37.3210   val Loss: 34.9216   time: 1.91s   best: 34.9216\n",
      "Epoch 526/1000:  train Loss: 37.2995   val Loss: 35.0752   time: 1.92s   best: 34.9216\n",
      "Epoch 527/1000:  train Loss: 37.3370   val Loss: 35.1859   time: 1.94s   best: 34.9216\n",
      "Epoch 528/1000:  train Loss: 37.2966   val Loss: 34.8444   time: 1.95s   best: 34.8444\n",
      "Epoch 529/1000:  train Loss: 37.3761   val Loss: 35.0562   time: 1.96s   best: 34.8444\n",
      "Epoch 530/1000:  train Loss: 37.2281   val Loss: 34.9106   time: 1.95s   best: 34.8444\n",
      "Epoch 531/1000:  train Loss: 37.2661   val Loss: 34.8984   time: 1.96s   best: 34.8444\n",
      "Epoch 532/1000:  train Loss: 37.2899   val Loss: 35.1021   time: 1.95s   best: 34.8444\n",
      "Epoch 533/1000:  train Loss: 37.2947   val Loss: 34.9840   time: 1.94s   best: 34.8444\n",
      "Epoch 534/1000:  train Loss: 37.3187   val Loss: 35.0989   time: 1.92s   best: 34.8444\n",
      "Epoch 535/1000:  train Loss: 37.3257   val Loss: 35.0388   time: 1.92s   best: 34.8444\n",
      "Epoch 536/1000:  train Loss: 37.1829   val Loss: 35.0238   time: 1.91s   best: 34.8444\n",
      "Epoch 537/1000:  train Loss: 37.1954   val Loss: 34.9630   time: 1.91s   best: 34.8444\n",
      "Epoch 538/1000:  train Loss: 37.1289   val Loss: 34.8176   time: 1.91s   best: 34.8176\n",
      "Epoch 539/1000:  train Loss: 37.1513   val Loss: 34.9421   time: 1.92s   best: 34.8176\n",
      "Epoch 540/1000:  train Loss: 37.1259   val Loss: 34.9697   time: 1.94s   best: 34.8176\n",
      "Epoch 541/1000:  train Loss: 37.0999   val Loss: 35.0942   time: 1.94s   best: 34.8176\n",
      "Epoch 542/1000:  train Loss: 37.1733   val Loss: 35.0887   time: 1.95s   best: 34.8176\n",
      "Epoch 543/1000:  train Loss: 37.1640   val Loss: 34.9003   time: 1.94s   best: 34.8176\n",
      "Epoch 544/1000:  train Loss: 37.0285   val Loss: 34.8135   time: 1.94s   best: 34.8135\n",
      "Epoch 545/1000:  train Loss: 37.1277   val Loss: 34.7827   time: 1.94s   best: 34.7827\n",
      "Epoch 546/1000:  train Loss: 37.0446   val Loss: 34.7617   time: 1.91s   best: 34.7617\n",
      "Epoch 547/1000:  train Loss: 37.1181   val Loss: 34.9866   time: 1.92s   best: 34.7617\n",
      "Epoch 548/1000:  train Loss: 37.0207   val Loss: 34.9197   time: 1.91s   best: 34.7617\n",
      "Epoch 549/1000:  train Loss: 37.1284   val Loss: 34.8791   time: 1.90s   best: 34.7617\n",
      "Epoch 550/1000:  train Loss: 37.0780   val Loss: 34.8722   time: 1.90s   best: 34.7617\n",
      "Epoch 551/1000:  train Loss: 37.1109   val Loss: 34.7761   time: 1.90s   best: 34.7617\n",
      "Epoch 552/1000:  train Loss: 37.0298   val Loss: 34.7408   time: 1.91s   best: 34.7408\n",
      "Epoch 553/1000:  train Loss: 37.0017   val Loss: 34.9367   time: 1.93s   best: 34.7408\n",
      "Epoch 554/1000:  train Loss: 36.9946   val Loss: 34.7918   time: 1.94s   best: 34.7408\n",
      "Epoch 555/1000:  train Loss: 36.9016   val Loss: 34.7882   time: 1.95s   best: 34.7408\n",
      "Epoch 556/1000:  train Loss: 36.9288   val Loss: 34.8169   time: 1.94s   best: 34.7408\n",
      "Epoch 557/1000:  train Loss: 36.8619   val Loss: 34.8780   time: 1.95s   best: 34.7408\n",
      "Epoch 558/1000:  train Loss: 36.8365   val Loss: 34.8595   time: 1.94s   best: 34.7408\n",
      "Epoch 559/1000:  train Loss: 36.9839   val Loss: 34.8289   time: 1.93s   best: 34.7408\n",
      "Epoch 560/1000:  train Loss: 36.8441   val Loss: 34.6596   time: 1.92s   best: 34.6596\n",
      "Epoch 561/1000:  train Loss: 36.8457   val Loss: 34.7458   time: 1.91s   best: 34.6596\n",
      "Epoch 562/1000:  train Loss: 36.8913   val Loss: 34.7688   time: 1.90s   best: 34.6596\n",
      "Epoch 563/1000:  train Loss: 36.8432   val Loss: 34.6691   time: 1.91s   best: 34.6596\n",
      "Epoch 564/1000:  train Loss: 36.8948   val Loss: 34.7763   time: 1.90s   best: 34.6596\n",
      "Epoch 565/1000:  train Loss: 36.8532   val Loss: 34.7344   time: 1.92s   best: 34.6596\n",
      "Epoch 566/1000:  train Loss: 36.8190   val Loss: 34.6833   time: 1.94s   best: 34.6596\n",
      "Epoch 567/1000:  train Loss: 36.8456   val Loss: 34.6236   time: 1.94s   best: 34.6236\n",
      "Epoch 568/1000:  train Loss: 36.8395   val Loss: 34.7792   time: 1.95s   best: 34.6236\n",
      "Epoch 569/1000:  train Loss: 36.9454   val Loss: 34.8438   time: 1.95s   best: 34.6236\n",
      "Epoch 570/1000:  train Loss: 36.7984   val Loss: 34.7418   time: 1.94s   best: 34.6236\n",
      "Epoch 571/1000:  train Loss: 36.7297   val Loss: 34.7915   time: 1.94s   best: 34.6236\n",
      "Epoch 572/1000:  train Loss: 36.7046   val Loss: 34.6496   time: 1.92s   best: 34.6236\n",
      "Epoch 573/1000:  train Loss: 36.7916   val Loss: 34.7488   time: 1.91s   best: 34.6236\n",
      "Epoch 574/1000:  train Loss: 36.6978   val Loss: 34.6029   time: 1.90s   best: 34.6029\n",
      "Epoch 575/1000:  train Loss: 36.6906   val Loss: 34.6200   time: 1.91s   best: 34.6029\n",
      "Epoch 576/1000:  train Loss: 36.7471   val Loss: 34.5477   time: 1.90s   best: 34.5477\n",
      "Epoch 577/1000:  train Loss: 36.7329   val Loss: 34.7572   time: 1.90s   best: 34.5477\n",
      "Epoch 578/1000:  train Loss: 36.6568   val Loss: 34.7439   time: 1.93s   best: 34.5477\n",
      "Epoch 579/1000:  train Loss: 36.5755   val Loss: 34.6951   time: 1.93s   best: 34.5477\n",
      "Epoch 580/1000:  train Loss: 36.6598   val Loss: 34.6344   time: 1.94s   best: 34.5477\n",
      "Epoch 581/1000:  train Loss: 36.6585   val Loss: 34.6307   time: 1.95s   best: 34.5477\n",
      "Epoch 582/1000:  train Loss: 36.5734   val Loss: 34.5371   time: 1.94s   best: 34.5371\n",
      "Epoch 583/1000:  train Loss: 36.6480   val Loss: 34.4785   time: 1.95s   best: 34.4785\n",
      "Epoch 584/1000:  train Loss: 36.6454   val Loss: 34.5446   time: 1.93s   best: 34.4785\n",
      "Epoch 585/1000:  train Loss: 36.6444   val Loss: 34.6303   time: 1.92s   best: 34.4785\n",
      "Epoch 586/1000:  train Loss: 36.5988   val Loss: 34.6713   time: 1.92s   best: 34.4785\n",
      "Epoch 587/1000:  train Loss: 36.5231   val Loss: 34.5771   time: 1.91s   best: 34.4785\n",
      "Epoch 588/1000:  train Loss: 36.5108   val Loss: 34.5029   time: 1.90s   best: 34.4785\n",
      "Epoch 589/1000:  train Loss: 36.5461   val Loss: 34.6044   time: 1.90s   best: 34.4785\n",
      "Epoch 590/1000:  train Loss: 36.5773   val Loss: 34.5491   time: 1.90s   best: 34.4785\n",
      "Epoch 591/1000:  train Loss: 36.5318   val Loss: 34.5337   time: 1.92s   best: 34.4785\n",
      "Epoch 592/1000:  train Loss: 36.5248   val Loss: 34.6525   time: 1.93s   best: 34.4785\n",
      "Epoch 593/1000:  train Loss: 36.4686   val Loss: 34.4938   time: 1.94s   best: 34.4785\n",
      "Epoch 594/1000:  train Loss: 36.3875   val Loss: 34.6788   time: 1.94s   best: 34.4785\n",
      "Epoch 595/1000:  train Loss: 36.4668   val Loss: 34.7406   time: 1.95s   best: 34.4785\n",
      "Epoch 596/1000:  train Loss: 36.4150   val Loss: 34.6285   time: 1.94s   best: 34.4785\n",
      "Epoch 597/1000:  train Loss: 36.5097   val Loss: 34.5388   time: 1.93s   best: 34.4785\n",
      "Epoch 598/1000:  train Loss: 36.4873   val Loss: 34.5857   time: 1.92s   best: 34.4785\n",
      "Epoch 599/1000:  train Loss: 36.4613   val Loss: 34.4155   time: 1.92s   best: 34.4155\n",
      "Epoch 600/1000:  train Loss: 36.3503   val Loss: 34.6154   time: 1.91s   best: 34.4155\n",
      "Epoch 601/1000:  train Loss: 36.3887   val Loss: 34.7223   time: 1.91s   best: 34.4155\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 602/1000:  train Loss: 36.4335   val Loss: 34.4307   time: 1.91s   best: 34.4155\n",
      "Epoch 603/1000:  train Loss: 36.3437   val Loss: 34.4828   time: 1.90s   best: 34.4155\n",
      "Epoch 604/1000:  train Loss: 36.2128   val Loss: 34.4305   time: 1.91s   best: 34.4155\n",
      "Epoch 605/1000:  train Loss: 36.3867   val Loss: 34.3570   time: 1.93s   best: 34.3570\n",
      "Epoch 606/1000:  train Loss: 36.2958   val Loss: 34.4787   time: 1.94s   best: 34.3570\n",
      "Epoch 607/1000:  train Loss: 36.3633   val Loss: 34.6351   time: 1.95s   best: 34.3570\n",
      "Epoch 608/1000:  train Loss: 36.2802   val Loss: 34.2991   time: 1.95s   best: 34.2991\n",
      "Epoch 609/1000:  train Loss: 36.3599   val Loss: 34.4123   time: 1.94s   best: 34.2991\n",
      "Epoch 610/1000:  train Loss: 36.2924   val Loss: 34.4831   time: 1.94s   best: 34.2991\n",
      "Epoch 611/1000:  train Loss: 36.2694   val Loss: 34.6120   time: 1.93s   best: 34.2991\n",
      "Epoch 612/1000:  train Loss: 36.2132   val Loss: 34.3917   time: 1.91s   best: 34.2991\n",
      "Epoch 613/1000:  train Loss: 36.3196   val Loss: 34.4636   time: 1.91s   best: 34.2991\n",
      "Epoch 614/1000:  train Loss: 36.2327   val Loss: 34.4158   time: 1.90s   best: 34.2991\n",
      "Epoch 615/1000:  train Loss: 36.1955   val Loss: 34.4494   time: 1.90s   best: 34.2991\n",
      "Epoch 616/1000:  train Loss: 36.2989   val Loss: 34.3854   time: 1.90s   best: 34.2991\n",
      "Epoch 617/1000:  train Loss: 36.1940   val Loss: 34.2737   time: 1.91s   best: 34.2737\n",
      "Epoch 618/1000:  train Loss: 36.2205   val Loss: 34.3809   time: 1.94s   best: 34.2737\n",
      "Epoch 619/1000:  train Loss: 36.2237   val Loss: 34.3664   time: 1.94s   best: 34.2737\n",
      "Epoch 620/1000:  train Loss: 36.1433   val Loss: 34.3074   time: 1.95s   best: 34.2737\n",
      "Epoch 621/1000:  train Loss: 36.2306   val Loss: 34.3754   time: 1.94s   best: 34.2737\n",
      "Epoch 622/1000:  train Loss: 36.1799   val Loss: 34.5071   time: 1.94s   best: 34.2737\n",
      "Epoch 623/1000:  train Loss: 36.2462   val Loss: 34.4314   time: 1.94s   best: 34.2737\n",
      "Epoch 624/1000:  train Loss: 36.0975   val Loss: 34.4234   time: 1.91s   best: 34.2737\n",
      "Epoch 625/1000:  train Loss: 36.1311   val Loss: 34.4267   time: 1.92s   best: 34.2737\n",
      "Epoch 626/1000:  train Loss: 36.1385   val Loss: 34.3726   time: 1.91s   best: 34.2737\n",
      "Epoch 627/1000:  train Loss: 36.1003   val Loss: 34.4543   time: 1.90s   best: 34.2737\n",
      "Epoch 628/1000:  train Loss: 36.1116   val Loss: 34.4690   time: 1.90s   best: 34.2737\n",
      "Epoch 629/1000:  train Loss: 36.0939   val Loss: 34.3330   time: 1.90s   best: 34.2737\n",
      "Epoch 630/1000:  train Loss: 36.0110   val Loss: 34.3314   time: 1.91s   best: 34.2737\n",
      "Epoch 631/1000:  train Loss: 36.0989   val Loss: 34.3842   time: 1.93s   best: 34.2737\n",
      "Epoch 632/1000:  train Loss: 36.0650   val Loss: 34.4063   time: 1.94s   best: 34.2737\n",
      "Epoch 633/1000:  train Loss: 35.9890   val Loss: 34.4907   time: 1.95s   best: 34.2737\n",
      "Epoch 634/1000:  train Loss: 35.9035   val Loss: 34.2897   time: 1.94s   best: 34.2737\n",
      "Epoch 635/1000:  train Loss: 35.9525   val Loss: 34.4554   time: 1.94s   best: 34.2737\n",
      "Epoch 636/1000:  train Loss: 36.0647   val Loss: 34.2801   time: 1.94s   best: 34.2737\n",
      "Epoch 637/1000:  train Loss: 36.0332   val Loss: 34.2976   time: 1.93s   best: 34.2737\n",
      "Epoch 638/1000:  train Loss: 35.9774   val Loss: 34.2937   time: 1.91s   best: 34.2737\n",
      "Epoch 639/1000:  train Loss: 36.0446   val Loss: 34.3367   time: 1.91s   best: 34.2737\n",
      "Epoch 640/1000:  train Loss: 35.9676   val Loss: 34.2527   time: 1.90s   best: 34.2527\n",
      "Epoch 641/1000:  train Loss: 35.9378   val Loss: 34.4771   time: 1.90s   best: 34.2527\n",
      "Epoch 642/1000:  train Loss: 35.9561   val Loss: 34.3253   time: 1.90s   best: 34.2527\n",
      "Epoch 643/1000:  train Loss: 35.9444   val Loss: 34.2605   time: 1.92s   best: 34.2527\n",
      "Epoch 644/1000:  train Loss: 35.8871   val Loss: 34.2266   time: 1.93s   best: 34.2266\n",
      "Epoch 645/1000:  train Loss: 35.7506   val Loss: 34.2104   time: 1.94s   best: 34.2104\n",
      "Epoch 646/1000:  train Loss: 35.9076   val Loss: 34.2727   time: 1.95s   best: 34.2104\n",
      "Epoch 647/1000:  train Loss: 35.8541   val Loss: 34.3745   time: 1.94s   best: 34.2104\n",
      "Epoch 648/1000:  train Loss: 35.7843   val Loss: 34.3519   time: 1.94s   best: 34.2104\n",
      "Epoch 649/1000:  train Loss: 35.8975   val Loss: 34.2613   time: 1.94s   best: 34.2104\n",
      "Epoch 650/1000:  train Loss: 35.7746   val Loss: 34.2756   time: 1.92s   best: 34.2104\n",
      "Epoch 651/1000:  train Loss: 35.8554   val Loss: 34.3212   time: 1.92s   best: 34.2104\n",
      "Epoch 652/1000:  train Loss: 35.8091   val Loss: 34.1549   time: 1.91s   best: 34.1549\n",
      "Epoch 653/1000:  train Loss: 35.7689   val Loss: 34.2799   time: 1.90s   best: 34.1549\n",
      "Epoch 654/1000:  train Loss: 35.7569   val Loss: 34.1919   time: 1.90s   best: 34.1549\n",
      "Epoch 655/1000:  train Loss: 35.8784   val Loss: 34.4327   time: 1.90s   best: 34.1549\n",
      "Epoch 656/1000:  train Loss: 35.8739   val Loss: 34.2726   time: 1.92s   best: 34.1549\n",
      "Epoch 657/1000:  train Loss: 35.8277   val Loss: 34.1770   time: 1.93s   best: 34.1549\n",
      "Epoch 658/1000:  train Loss: 35.8859   val Loss: 34.2150   time: 1.94s   best: 34.1549\n",
      "Epoch 659/1000:  train Loss: 35.6985   val Loss: 34.3951   time: 1.94s   best: 34.1549\n",
      "Epoch 660/1000:  train Loss: 35.8326   val Loss: 34.1759   time: 1.94s   best: 34.1549\n",
      "Epoch 661/1000:  train Loss: 35.7202   val Loss: 34.0766   time: 1.95s   best: 34.0766\n",
      "Epoch 662/1000:  train Loss: 35.6976   val Loss: 34.5777   time: 1.93s   best: 34.0766\n",
      "Epoch 663/1000:  train Loss: 35.8539   val Loss: 34.1367   time: 1.92s   best: 34.0766\n",
      "Epoch 664/1000:  train Loss: 35.6713   val Loss: 34.2297   time: 1.92s   best: 34.0766\n",
      "Epoch 665/1000:  train Loss: 35.6928   val Loss: 34.1789   time: 1.91s   best: 34.0766\n",
      "Epoch 666/1000:  train Loss: 35.6309   val Loss: 34.1285   time: 1.90s   best: 34.0766\n",
      "Epoch 667/1000:  train Loss: 35.7086   val Loss: 34.0283   time: 1.90s   best: 34.0283\n",
      "Epoch 668/1000:  train Loss: 35.6667   val Loss: 34.1836   time: 1.90s   best: 34.0283\n",
      "Epoch 669/1000:  train Loss: 35.6395   val Loss: 34.1459   time: 1.92s   best: 34.0283\n",
      "Epoch 670/1000:  train Loss: 35.5929   val Loss: 34.0737   time: 1.93s   best: 34.0283\n",
      "Epoch 671/1000:  train Loss: 35.6550   val Loss: 33.9639   time: 1.94s   best: 33.9639\n",
      "Epoch 672/1000:  train Loss: 35.6017   val Loss: 34.0567   time: 1.94s   best: 33.9639\n",
      "Epoch 673/1000:  train Loss: 35.5887   val Loss: 34.1101   time: 1.94s   best: 33.9639\n",
      "Epoch 674/1000:  train Loss: 35.5798   val Loss: 34.1503   time: 1.94s   best: 33.9639\n",
      "Epoch 675/1000:  train Loss: 35.6962   val Loss: 34.0210   time: 1.93s   best: 33.9639\n",
      "Epoch 676/1000:  train Loss: 35.5484   val Loss: 34.2548   time: 1.92s   best: 33.9639\n",
      "Epoch 677/1000:  train Loss: 35.5926   val Loss: 34.1221   time: 1.92s   best: 33.9639\n",
      "Epoch 678/1000:  train Loss: 35.5270   val Loss: 34.0409   time: 1.91s   best: 33.9639\n",
      "Epoch 679/1000:  train Loss: 35.5518   val Loss: 34.2432   time: 1.90s   best: 33.9639\n",
      "Epoch 680/1000:  train Loss: 35.5754   val Loss: 34.0784   time: 1.90s   best: 33.9639\n",
      "Epoch 681/1000:  train Loss: 35.5012   val Loss: 34.3384   time: 1.90s   best: 33.9639\n",
      "Epoch 682/1000:  train Loss: 35.5977   val Loss: 34.2003   time: 1.91s   best: 33.9639\n",
      "Epoch 683/1000:  train Loss: 35.5413   val Loss: 34.0481   time: 1.93s   best: 33.9639\n",
      "Epoch 684/1000:  train Loss: 35.5464   val Loss: 34.0219   time: 1.95s   best: 33.9639\n",
      "Epoch 685/1000:  train Loss: 35.5113   val Loss: 34.0167   time: 1.95s   best: 33.9639\n",
      "Epoch 686/1000:  train Loss: 35.5019   val Loss: 34.1519   time: 1.95s   best: 33.9639\n",
      "Epoch 687/1000:  train Loss: 35.4884   val Loss: 34.1004   time: 1.95s   best: 33.9639\n",
      "Epoch 688/1000:  train Loss: 35.4832   val Loss: 34.1258   time: 1.95s   best: 33.9639\n",
      "Epoch 689/1000:  train Loss: 35.4581   val Loss: 33.8857   time: 1.93s   best: 33.8857\n",
      "Epoch 690/1000:  train Loss: 35.4832   val Loss: 34.2183   time: 1.92s   best: 33.8857\n",
      "Epoch 691/1000:  train Loss: 35.4561   val Loss: 33.9725   time: 1.91s   best: 33.8857\n",
      "Epoch 692/1000:  train Loss: 35.3727   val Loss: 33.9970   time: 1.91s   best: 33.8857\n",
      "Epoch 693/1000:  train Loss: 35.3416   val Loss: 33.9924   time: 1.91s   best: 33.8857\n",
      "Epoch 694/1000:  train Loss: 35.4360   val Loss: 33.9187   time: 1.91s   best: 33.8857\n",
      "Epoch 695/1000:  train Loss: 35.3555   val Loss: 33.9960   time: 1.92s   best: 33.8857\n",
      "Epoch 696/1000:  train Loss: 35.4366   val Loss: 33.9098   time: 1.95s   best: 33.8857\n",
      "Epoch 697/1000:  train Loss: 35.3140   val Loss: 34.1553   time: 1.95s   best: 33.8857\n",
      "Epoch 698/1000:  train Loss: 35.3458   val Loss: 34.1477   time: 1.95s   best: 33.8857\n",
      "Epoch 699/1000:  train Loss: 35.3980   val Loss: 33.9745   time: 1.95s   best: 33.8857\n",
      "Epoch 700/1000:  train Loss: 35.3658   val Loss: 34.0440   time: 1.95s   best: 33.8857\n",
      "Epoch 701/1000:  train Loss: 35.3466   val Loss: 33.8992   time: 1.95s   best: 33.8857\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 702/1000:  train Loss: 35.2902   val Loss: 33.9641   time: 1.92s   best: 33.8857\n",
      "Epoch 703/1000:  train Loss: 35.4353   val Loss: 34.2471   time: 1.93s   best: 33.8857\n",
      "Epoch 704/1000:  train Loss: 35.3429   val Loss: 34.0908   time: 1.92s   best: 33.8857\n",
      "Epoch 705/1000:  train Loss: 35.2109   val Loss: 34.1166   time: 1.91s   best: 33.8857\n",
      "Epoch 706/1000:  train Loss: 35.3082   val Loss: 33.8142   time: 1.91s   best: 33.8142\n",
      "Epoch 707/1000:  train Loss: 35.2735   val Loss: 33.9923   time: 1.91s   best: 33.8142\n",
      "Epoch 708/1000:  train Loss: 35.2338   val Loss: 34.0651   time: 1.92s   best: 33.8142\n",
      "Epoch 709/1000:  train Loss: 35.2197   val Loss: 34.1875   time: 1.94s   best: 33.8142\n",
      "Epoch 710/1000:  train Loss: 35.3215   val Loss: 34.0346   time: 1.95s   best: 33.8142\n",
      "Epoch 711/1000:  train Loss: 35.2174   val Loss: 34.0565   time: 1.96s   best: 33.8142\n",
      "Epoch 712/1000:  train Loss: 35.2662   val Loss: 33.8232   time: 1.95s   best: 33.8142\n",
      "Epoch 713/1000:  train Loss: 35.2424   val Loss: 34.0277   time: 1.95s   best: 33.8142\n",
      "Epoch 714/1000:  train Loss: 35.1498   val Loss: 33.8643   time: 1.95s   best: 33.8142\n",
      "Epoch 715/1000:  train Loss: 35.2154   val Loss: 33.8569   time: 1.94s   best: 33.8142\n",
      "Epoch 716/1000:  train Loss: 35.1839   val Loss: 33.7816   time: 1.93s   best: 33.7816\n",
      "Epoch 717/1000:  train Loss: 35.2107   val Loss: 33.9324   time: 1.92s   best: 33.7816\n",
      "Epoch 718/1000:  train Loss: 35.2306   val Loss: 33.9326   time: 1.91s   best: 33.7816\n",
      "Epoch 719/1000:  train Loss: 35.2028   val Loss: 33.9030   time: 1.91s   best: 33.7816\n",
      "Epoch 720/1000:  train Loss: 35.2040   val Loss: 33.9924   time: 1.91s   best: 33.7816\n",
      "Epoch 721/1000:  train Loss: 35.1124   val Loss: 34.0376   time: 1.92s   best: 33.7816\n",
      "Epoch 722/1000:  train Loss: 35.1482   val Loss: 33.8349   time: 1.94s   best: 33.7816\n",
      "Epoch 723/1000:  train Loss: 35.1116   val Loss: 33.8543   time: 1.95s   best: 33.7816\n",
      "Epoch 724/1000:  train Loss: 35.0867   val Loss: 33.8482   time: 1.95s   best: 33.7816\n",
      "Epoch 725/1000:  train Loss: 35.1058   val Loss: 33.9305   time: 1.95s   best: 33.7816\n",
      "Epoch 726/1000:  train Loss: 35.1100   val Loss: 33.9031   time: 1.95s   best: 33.7816\n",
      "Epoch 727/1000:  train Loss: 35.1128   val Loss: 33.7709   time: 1.95s   best: 33.7709\n",
      "Epoch 728/1000:  train Loss: 35.0431   val Loss: 33.7861   time: 1.93s   best: 33.7709\n",
      "Epoch 729/1000:  train Loss: 35.0263   val Loss: 33.8344   time: 1.93s   best: 33.7709\n",
      "Epoch 730/1000:  train Loss: 35.1757   val Loss: 33.9594   time: 1.92s   best: 33.7709\n",
      "Epoch 731/1000:  train Loss: 34.9946   val Loss: 33.8658   time: 1.91s   best: 33.7709\n",
      "Epoch 732/1000:  train Loss: 35.0963   val Loss: 33.9144   time: 1.91s   best: 33.7709\n",
      "Epoch 733/1000:  train Loss: 35.1147   val Loss: 33.9085   time: 1.91s   best: 33.7709\n",
      "Epoch 734/1000:  train Loss: 35.0684   val Loss: 33.8024   time: 1.94s   best: 33.7709\n",
      "Epoch 735/1000:  train Loss: 34.9249   val Loss: 33.6558   time: 1.94s   best: 33.6558\n",
      "Epoch 736/1000:  train Loss: 34.8542   val Loss: 33.9079   time: 1.95s   best: 33.6558\n",
      "Epoch 737/1000:  train Loss: 35.0044   val Loss: 33.9217   time: 1.96s   best: 33.6558\n",
      "Epoch 738/1000:  train Loss: 35.1090   val Loss: 33.7851   time: 1.95s   best: 33.6558\n",
      "Epoch 739/1000:  train Loss: 35.0398   val Loss: 33.8722   time: 1.95s   best: 33.6558\n",
      "Epoch 740/1000:  train Loss: 34.9677   val Loss: 34.1157   time: 1.94s   best: 33.6558\n",
      "Epoch 741/1000:  train Loss: 35.0846   val Loss: 33.7046   time: 1.93s   best: 33.6558\n",
      "Epoch 742/1000:  train Loss: 34.8748   val Loss: 33.6245   time: 1.93s   best: 33.6245\n",
      "Epoch 743/1000:  train Loss: 34.9064   val Loss: 33.6491   time: 1.91s   best: 33.6245\n",
      "Epoch 744/1000:  train Loss: 34.9538   val Loss: 33.8952   time: 1.90s   best: 33.6245\n",
      "Epoch 745/1000:  train Loss: 34.9501   val Loss: 33.6665   time: 1.90s   best: 33.6245\n",
      "Epoch 746/1000:  train Loss: 35.0274   val Loss: 33.8644   time: 1.91s   best: 33.6245\n",
      "Epoch 747/1000:  train Loss: 34.9690   val Loss: 33.7024   time: 1.93s   best: 33.6245\n",
      "Epoch 748/1000:  train Loss: 34.9129   val Loss: 34.2240   time: 1.94s   best: 33.6245\n",
      "Epoch 749/1000:  train Loss: 34.8937   val Loss: 33.8633   time: 1.94s   best: 33.6245\n",
      "Epoch 750/1000:  train Loss: 34.9084   val Loss: 33.8066   time: 1.94s   best: 33.6245\n",
      "Epoch 751/1000:  train Loss: 34.8268   val Loss: 33.7024   time: 1.95s   best: 33.6245\n",
      "Epoch 752/1000:  train Loss: 34.8093   val Loss: 33.8408   time: 1.94s   best: 33.6245\n",
      "Epoch 753/1000:  train Loss: 34.8698   val Loss: 33.9279   time: 1.93s   best: 33.6245\n",
      "Epoch 754/1000:  train Loss: 34.7944   val Loss: 33.9565   time: 1.92s   best: 33.6245\n",
      "Epoch 755/1000:  train Loss: 34.8598   val Loss: 33.8311   time: 1.92s   best: 33.6245\n",
      "Epoch 756/1000:  train Loss: 34.9534   val Loss: 33.6705   time: 1.91s   best: 33.6245\n",
      "Epoch 757/1000:  train Loss: 34.7628   val Loss: 33.7549   time: 1.91s   best: 33.6245\n",
      "Epoch 758/1000:  train Loss: 34.8330   val Loss: 33.5725   time: 1.90s   best: 33.5725\n",
      "Epoch 759/1000:  train Loss: 34.8563   val Loss: 33.5916   time: 1.91s   best: 33.5725\n",
      "Epoch 760/1000:  train Loss: 34.8172   val Loss: 33.7180   time: 1.93s   best: 33.5725\n",
      "Epoch 761/1000:  train Loss: 34.8821   val Loss: 33.8279   time: 1.94s   best: 33.5725\n",
      "Epoch 762/1000:  train Loss: 34.6586   val Loss: 33.7280   time: 1.95s   best: 33.5725\n",
      "Epoch 763/1000:  train Loss: 34.7924   val Loss: 33.7146   time: 1.95s   best: 33.5725\n",
      "Epoch 764/1000:  train Loss: 34.7632   val Loss: 33.6763   time: 1.94s   best: 33.5725\n",
      "Epoch 765/1000:  train Loss: 34.6964   val Loss: 34.0647   time: 1.95s   best: 33.5725\n",
      "Epoch 766/1000:  train Loss: 34.7154   val Loss: 33.7757   time: 1.93s   best: 33.5725\n",
      "Epoch 767/1000:  train Loss: 34.7311   val Loss: 33.8588   time: 1.92s   best: 33.5725\n",
      "Epoch 768/1000:  train Loss: 34.6914   val Loss: 33.5884   time: 1.91s   best: 33.5725\n",
      "Epoch 769/1000:  train Loss: 34.7782   val Loss: 33.7488   time: 1.91s   best: 33.5725\n",
      "Epoch 770/1000:  train Loss: 34.7263   val Loss: 33.7108   time: 1.90s   best: 33.5725\n",
      "Epoch 771/1000:  train Loss: 34.6573   val Loss: 33.8921   time: 1.90s   best: 33.5725\n",
      "Epoch 772/1000:  train Loss: 34.6850   val Loss: 33.6985   time: 1.91s   best: 33.5725\n",
      "Epoch 773/1000:  train Loss: 34.5644   val Loss: 33.7113   time: 1.93s   best: 33.5725\n",
      "Epoch 774/1000:  train Loss: 34.6890   val Loss: 33.8068   time: 1.94s   best: 33.5725\n",
      "Epoch 775/1000:  train Loss: 34.6027   val Loss: 33.6990   time: 1.95s   best: 33.5725\n",
      "Epoch 776/1000:  train Loss: 34.6489   val Loss: 33.6793   time: 1.95s   best: 33.5725\n",
      "Epoch 777/1000:  train Loss: 34.5979   val Loss: 33.6073   time: 1.95s   best: 33.5725\n",
      "Epoch 778/1000:  train Loss: 34.6729   val Loss: 33.7050   time: 1.94s   best: 33.5725\n",
      "Epoch 779/1000:  train Loss: 34.5873   val Loss: 33.6631   time: 1.93s   best: 33.5725\n",
      "Epoch 780/1000:  train Loss: 34.7081   val Loss: 33.7460   time: 1.91s   best: 33.5725\n",
      "Epoch 781/1000:  train Loss: 34.6125   val Loss: 33.5824   time: 1.92s   best: 33.5725\n",
      "Epoch 782/1000:  train Loss: 34.4956   val Loss: 33.5825   time: 1.91s   best: 33.5725\n",
      "Epoch 783/1000:  train Loss: 34.5872   val Loss: 33.4908   time: 1.90s   best: 33.4908\n",
      "Epoch 784/1000:  train Loss: 34.6833   val Loss: 33.6575   time: 1.90s   best: 33.4908\n",
      "Epoch 785/1000:  train Loss: 34.6727   val Loss: 33.6087   time: 1.90s   best: 33.4908\n",
      "Epoch 786/1000:  train Loss: 34.6103   val Loss: 33.7000   time: 1.91s   best: 33.4908\n",
      "Epoch 787/1000:  train Loss: 34.6548   val Loss: 33.5661   time: 1.94s   best: 33.4908\n",
      "Epoch 788/1000:  train Loss: 34.6232   val Loss: 33.6698   time: 1.95s   best: 33.4908\n",
      "Epoch 789/1000:  train Loss: 34.5675   val Loss: 33.6841   time: 1.95s   best: 33.4908\n",
      "Epoch 790/1000:  train Loss: 34.6346   val Loss: 33.8179   time: 1.95s   best: 33.4908\n",
      "Epoch 791/1000:  train Loss: 34.4850   val Loss: 33.3904   time: 1.95s   best: 33.3904\n",
      "Epoch 792/1000:  train Loss: 34.4247   val Loss: 33.5476   time: 1.94s   best: 33.3904\n",
      "Epoch 793/1000:  train Loss: 34.5180   val Loss: 33.5136   time: 1.94s   best: 33.3904\n",
      "Epoch 794/1000:  train Loss: 34.4711   val Loss: 33.5385   time: 1.92s   best: 33.3904\n",
      "Epoch 795/1000:  train Loss: 34.5067   val Loss: 33.5191   time: 1.91s   best: 33.3904\n",
      "Epoch 796/1000:  train Loss: 34.4755   val Loss: 33.4986   time: 1.90s   best: 33.3904\n",
      "Epoch 797/1000:  train Loss: 34.4879   val Loss: 33.8971   time: 1.90s   best: 33.3904\n",
      "Epoch 798/1000:  train Loss: 34.5360   val Loss: 33.6143   time: 1.90s   best: 33.3904\n",
      "Epoch 799/1000:  train Loss: 34.4311   val Loss: 33.7012   time: 1.92s   best: 33.3904\n",
      "Epoch 800/1000:  train Loss: 34.4568   val Loss: 33.5684   time: 1.94s   best: 33.3904\n",
      "Epoch 801/1000:  train Loss: 34.4322   val Loss: 33.4839   time: 1.95s   best: 33.3904\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 802/1000:  train Loss: 34.3752   val Loss: 33.4815   time: 1.95s   best: 33.3904\n",
      "Epoch 803/1000:  train Loss: 34.4227   val Loss: 33.5375   time: 1.95s   best: 33.3904\n",
      "Epoch 804/1000:  train Loss: 34.3139   val Loss: 33.5917   time: 1.94s   best: 33.3904\n",
      "Epoch 805/1000:  train Loss: 34.4094   val Loss: 33.6295   time: 1.94s   best: 33.3904\n",
      "Epoch 806/1000:  train Loss: 34.4307   val Loss: 33.5983   time: 1.93s   best: 33.3904\n",
      "Epoch 807/1000:  train Loss: 34.4188   val Loss: 33.4658   time: 1.92s   best: 33.3904\n",
      "Epoch 808/1000:  train Loss: 34.3129   val Loss: 33.7611   time: 1.91s   best: 33.3904\n",
      "Epoch 809/1000:  train Loss: 34.5171   val Loss: 33.7330   time: 1.91s   best: 33.3904\n",
      "Epoch 810/1000:  train Loss: 34.3656   val Loss: 33.6101   time: 1.91s   best: 33.3904\n",
      "Epoch 811/1000:  train Loss: 34.4646   val Loss: 33.7477   time: 1.91s   best: 33.3904\n",
      "Epoch 812/1000:  train Loss: 34.2741   val Loss: 33.5933   time: 1.94s   best: 33.3904\n",
      "Epoch 813/1000:  train Loss: 34.3065   val Loss: 33.5314   time: 1.94s   best: 33.3904\n",
      "Epoch 814/1000:  train Loss: 34.3433   val Loss: 33.8389   time: 1.95s   best: 33.3904\n",
      "Epoch 815/1000:  train Loss: 34.2621   val Loss: 33.4467   time: 1.95s   best: 33.3904\n",
      "Epoch 816/1000:  train Loss: 34.2136   val Loss: 33.4591   time: 1.95s   best: 33.3904\n",
      "Epoch 817/1000:  train Loss: 34.2781   val Loss: 33.5172   time: 1.95s   best: 33.3904\n",
      "Epoch 818/1000:  train Loss: 34.3216   val Loss: 33.5315   time: 1.93s   best: 33.3904\n",
      "Epoch 819/1000:  train Loss: 34.1655   val Loss: 33.3281   time: 1.92s   best: 33.3281\n",
      "Epoch 820/1000:  train Loss: 34.4168   val Loss: 33.6689   time: 1.92s   best: 33.3281\n",
      "Epoch 821/1000:  train Loss: 34.3752   val Loss: 33.6281   time: 1.91s   best: 33.3281\n",
      "Epoch 822/1000:  train Loss: 34.2211   val Loss: 33.4139   time: 1.91s   best: 33.3281\n",
      "Epoch 823/1000:  train Loss: 34.2250   val Loss: 33.5735   time: 1.91s   best: 33.3281\n",
      "Epoch 824/1000:  train Loss: 34.3000   val Loss: 33.4592   time: 1.91s   best: 33.3281\n",
      "Epoch 825/1000:  train Loss: 34.2335   val Loss: 33.4215   time: 1.93s   best: 33.3281\n",
      "Epoch 826/1000:  train Loss: 34.2822   val Loss: 33.5113   time: 1.94s   best: 33.3281\n",
      "Epoch 827/1000:  train Loss: 34.2498   val Loss: 33.4626   time: 1.95s   best: 33.3281\n",
      "Epoch 828/1000:  train Loss: 34.1228   val Loss: 33.5932   time: 1.95s   best: 33.3281\n",
      "Epoch 829/1000:  train Loss: 34.1052   val Loss: 33.4073   time: 1.95s   best: 33.3281\n",
      "Epoch 830/1000:  train Loss: 34.2461   val Loss: 33.4968   time: 1.94s   best: 33.3281\n",
      "Epoch 831/1000:  train Loss: 34.1505   val Loss: 33.4745   time: 1.93s   best: 33.3281\n",
      "Epoch 832/1000:  train Loss: 34.2909   val Loss: 33.6910   time: 1.92s   best: 33.3281\n",
      "Epoch 833/1000:  train Loss: 34.2639   val Loss: 33.4413   time: 1.92s   best: 33.3281\n",
      "Epoch 834/1000:  train Loss: 34.1049   val Loss: 33.4152   time: 1.91s   best: 33.3281\n",
      "Epoch 835/1000:  train Loss: 34.2101   val Loss: 33.6211   time: 1.91s   best: 33.3281\n",
      "Epoch 836/1000:  train Loss: 34.1176   val Loss: 33.3377   time: 1.90s   best: 33.3281\n",
      "Epoch 837/1000:  train Loss: 34.2062   val Loss: 33.3393   time: 1.91s   best: 33.3281\n",
      "Epoch 838/1000:  train Loss: 34.1652   val Loss: 33.4149   time: 1.94s   best: 33.3281\n",
      "Epoch 839/1000:  train Loss: 34.0833   val Loss: 33.3698   time: 1.95s   best: 33.3281\n",
      "Epoch 840/1000:  train Loss: 34.1505   val Loss: 33.2622   time: 1.96s   best: 33.2622\n",
      "Epoch 841/1000:  train Loss: 34.1271   val Loss: 33.4760   time: 1.96s   best: 33.2622\n",
      "Epoch 842/1000:  train Loss: 34.1266   val Loss: 33.5123   time: 1.95s   best: 33.2622\n",
      "Epoch 843/1000:  train Loss: 34.0544   val Loss: 33.6605   time: 1.96s   best: 33.2622\n",
      "Epoch 844/1000:  train Loss: 34.1294   val Loss: 33.6275   time: 1.94s   best: 33.2622\n",
      "Epoch 845/1000:  train Loss: 34.1173   val Loss: 33.4049   time: 1.93s   best: 33.2622\n",
      "Epoch 846/1000:  train Loss: 34.0812   val Loss: 33.5299   time: 1.92s   best: 33.2622\n",
      "Epoch 847/1000:  train Loss: 34.0092   val Loss: 33.4830   time: 1.92s   best: 33.2622\n",
      "Epoch 848/1000:  train Loss: 34.0440   val Loss: 33.5896   time: 1.91s   best: 33.2622\n",
      "Epoch 849/1000:  train Loss: 34.1042   val Loss: 33.4639   time: 1.91s   best: 33.2622\n",
      "Epoch 850/1000:  train Loss: 34.0195   val Loss: 33.3187   time: 1.92s   best: 33.2622\n",
      "Epoch 851/1000:  train Loss: 33.9932   val Loss: 33.5944   time: 1.95s   best: 33.2622\n",
      "Epoch 852/1000:  train Loss: 34.0294   val Loss: 33.3720   time: 1.95s   best: 33.2622\n",
      "Epoch 853/1000:  train Loss: 34.1452   val Loss: 33.2363   time: 1.96s   best: 33.2363\n",
      "Epoch 854/1000:  train Loss: 33.8815   val Loss: 33.4478   time: 1.95s   best: 33.2363\n",
      "Epoch 855/1000:  train Loss: 34.0455   val Loss: 33.4225   time: 1.95s   best: 33.2363\n",
      "Epoch 856/1000:  train Loss: 34.0803   val Loss: 33.3594   time: 1.95s   best: 33.2363\n",
      "Epoch 857/1000:  train Loss: 33.9379   val Loss: 33.3145   time: 1.93s   best: 33.2363\n",
      "Epoch 858/1000:  train Loss: 33.8882   val Loss: 33.4159   time: 1.93s   best: 33.2363\n",
      "Epoch 859/1000:  train Loss: 33.9823   val Loss: 33.4848   time: 1.92s   best: 33.2363\n",
      "Epoch 860/1000:  train Loss: 34.0744   val Loss: 33.2182   time: 1.91s   best: 33.2182\n",
      "Epoch 861/1000:  train Loss: 33.9742   val Loss: 33.5019   time: 1.91s   best: 33.2182\n",
      "Epoch 862/1000:  train Loss: 33.9427   val Loss: 33.2734   time: 1.91s   best: 33.2182\n",
      "Epoch 863/1000:  train Loss: 33.9745   val Loss: 33.5171   time: 1.93s   best: 33.2182\n",
      "Epoch 864/1000:  train Loss: 34.1610   val Loss: 33.4930   time: 1.95s   best: 33.2182\n",
      "Epoch 865/1000:  train Loss: 33.8975   val Loss: 33.3925   time: 1.95s   best: 33.2182\n",
      "Epoch 866/1000:  train Loss: 33.9763   val Loss: 33.3204   time: 1.96s   best: 33.2182\n",
      "Epoch 867/1000:  train Loss: 33.9290   val Loss: 33.4419   time: 1.95s   best: 33.2182\n",
      "Epoch 868/1000:  train Loss: 33.8398   val Loss: 33.4928   time: 1.95s   best: 33.2182\n",
      "Epoch 869/1000:  train Loss: 33.8168   val Loss: 33.2935   time: 1.95s   best: 33.2182\n",
      "Epoch 870/1000:  train Loss: 33.8170   val Loss: 33.5109   time: 1.94s   best: 33.2182\n",
      "Epoch 871/1000:  train Loss: 33.8171   val Loss: 33.2519   time: 1.93s   best: 33.2182\n",
      "Epoch 872/1000:  train Loss: 33.7885   val Loss: 33.4295   time: 1.91s   best: 33.2182\n",
      "Epoch 873/1000:  train Loss: 33.8997   val Loss: 33.4762   time: 1.90s   best: 33.2182\n",
      "Epoch 874/1000:  train Loss: 33.7622   val Loss: 33.5469   time: 1.90s   best: 33.2182\n",
      "Epoch 875/1000:  train Loss: 33.7793   val Loss: 33.4001   time: 1.90s   best: 33.2182\n",
      "Epoch 876/1000:  train Loss: 33.8445   val Loss: 33.5733   time: 1.92s   best: 33.2182\n",
      "Epoch 877/1000:  train Loss: 33.8804   val Loss: 33.4387   time: 1.93s   best: 33.2182\n",
      "Epoch 878/1000:  train Loss: 33.8205   val Loss: 33.3137   time: 1.94s   best: 33.2182\n",
      "Epoch 879/1000:  train Loss: 33.9040   val Loss: 33.2480   time: 1.97s   best: 33.2182\n",
      "Epoch 880/1000:  train Loss: 33.8654   val Loss: 33.3715   time: 1.97s   best: 33.2182\n",
      "Epoch 881/1000:  train Loss: 33.8321   val Loss: 33.3587   time: 1.95s   best: 33.2182\n",
      "Epoch 882/1000:  train Loss: 33.8400   val Loss: 33.3908   time: 1.94s   best: 33.2182\n",
      "Epoch 883/1000:  train Loss: 33.7907   val Loss: 33.3787   time: 1.94s   best: 33.2182\n",
      "Epoch 884/1000:  train Loss: 33.8510   val Loss: 33.3423   time: 1.94s   best: 33.2182\n",
      "Epoch 885/1000:  train Loss: 33.6644   val Loss: 33.3328   time: 1.91s   best: 33.2182\n",
      "Epoch 886/1000:  train Loss: 33.8200   val Loss: 33.4349   time: 1.90s   best: 33.2182\n",
      "Epoch 887/1000:  train Loss: 33.7401   val Loss: 33.2979   time: 1.90s   best: 33.2182\n",
      "Epoch 888/1000:  train Loss: 33.7185   val Loss: 33.3339   time: 1.90s   best: 33.2182\n",
      "Epoch 889/1000:  train Loss: 33.7607   val Loss: 33.4117   time: 1.92s   best: 33.2182\n",
      "Epoch 890/1000:  train Loss: 33.7608   val Loss: 33.2089   time: 1.93s   best: 33.2089\n",
      "Epoch 891/1000:  train Loss: 33.7149   val Loss: 33.4410   time: 1.93s   best: 33.2089\n",
      "Epoch 892/1000:  train Loss: 33.8409   val Loss: 33.4623   time: 1.94s   best: 33.2089\n",
      "Epoch 893/1000:  train Loss: 33.7257   val Loss: 33.3435   time: 1.94s   best: 33.2089\n",
      "Epoch 894/1000:  train Loss: 33.6987   val Loss: 33.2689   time: 1.94s   best: 33.2089\n",
      "Epoch 895/1000:  train Loss: 33.7122   val Loss: 33.2722   time: 1.93s   best: 33.2089\n",
      "Epoch 896/1000:  train Loss: 33.5928   val Loss: 33.3869   time: 1.92s   best: 33.2089\n",
      "Epoch 897/1000:  train Loss: 33.6784   val Loss: 33.2629   time: 1.92s   best: 33.2089\n",
      "Epoch 898/1000:  train Loss: 33.6905   val Loss: 33.4814   time: 1.91s   best: 33.2089\n",
      "Epoch 899/1000:  train Loss: 33.6018   val Loss: 33.2988   time: 1.90s   best: 33.2089\n",
      "Epoch 900/1000:  train Loss: 33.6499   val Loss: 33.3817   time: 1.90s   best: 33.2089\n",
      "Epoch 901/1000:  train Loss: 33.6955   val Loss: 33.1983   time: 1.90s   best: 33.1983\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 902/1000:  train Loss: 33.5983   val Loss: 33.3268   time: 1.92s   best: 33.1983\n",
      "Epoch 903/1000:  train Loss: 33.6732   val Loss: 33.2870   time: 1.93s   best: 33.1983\n",
      "Epoch 904/1000:  train Loss: 33.7356   val Loss: 33.3028   time: 1.94s   best: 33.1983\n",
      "Epoch 905/1000:  train Loss: 33.7279   val Loss: 33.3948   time: 1.94s   best: 33.1983\n",
      "Epoch 906/1000:  train Loss: 33.6174   val Loss: 33.2950   time: 1.94s   best: 33.1983\n",
      "Epoch 907/1000:  train Loss: 33.6509   val Loss: 33.5391   time: 1.94s   best: 33.1983\n",
      "Epoch 908/1000:  train Loss: 33.7033   val Loss: 33.1954   time: 1.93s   best: 33.1954\n",
      "Epoch 909/1000:  train Loss: 33.6773   val Loss: 33.1198   time: 1.93s   best: 33.1198\n",
      "Epoch 910/1000:  train Loss: 33.5456   val Loss: 33.2253   time: 1.92s   best: 33.1198\n",
      "Epoch 911/1000:  train Loss: 33.5810   val Loss: 33.2159   time: 1.91s   best: 33.1198\n",
      "Epoch 912/1000:  train Loss: 33.6529   val Loss: 33.2197   time: 1.90s   best: 33.1198\n",
      "Epoch 913/1000:  train Loss: 33.6201   val Loss: 33.1468   time: 1.90s   best: 33.1198\n",
      "Epoch 914/1000:  train Loss: 33.5579   val Loss: 33.1931   time: 1.90s   best: 33.1198\n",
      "Epoch 915/1000:  train Loss: 33.6025   val Loss: 33.1945   time: 1.91s   best: 33.1198\n",
      "Epoch 916/1000:  train Loss: 33.5953   val Loss: 33.2565   time: 1.93s   best: 33.1198\n",
      "Epoch 917/1000:  train Loss: 33.4915   val Loss: 33.2157   time: 1.94s   best: 33.1198\n",
      "Epoch 918/1000:  train Loss: 33.5192   val Loss: 33.2965   time: 1.95s   best: 33.1198\n",
      "Epoch 919/1000:  train Loss: 33.5227   val Loss: 33.3817   time: 1.95s   best: 33.1198\n",
      "Epoch 920/1000:  train Loss: 33.5468   val Loss: 33.3314   time: 1.94s   best: 33.1198\n",
      "Epoch 921/1000:  train Loss: 33.5158   val Loss: 33.3260   time: 1.94s   best: 33.1198\n",
      "Epoch 922/1000:  train Loss: 33.6140   val Loss: 33.3088   time: 1.92s   best: 33.1198\n",
      "Epoch 923/1000:  train Loss: 33.5654   val Loss: 33.3581   time: 1.92s   best: 33.1198\n",
      "Epoch 924/1000:  train Loss: 33.5156   val Loss: 33.2504   time: 1.91s   best: 33.1198\n",
      "Epoch 925/1000:  train Loss: 33.4046   val Loss: 33.2796   time: 1.90s   best: 33.1198\n",
      "Epoch 926/1000:  train Loss: 33.4783   val Loss: 33.2928   time: 1.90s   best: 33.1198\n",
      "Epoch 927/1000:  train Loss: 33.5342   val Loss: 33.1870   time: 1.90s   best: 33.1198\n",
      "Epoch 928/1000:  train Loss: 33.4424   val Loss: 33.2443   time: 1.91s   best: 33.1198\n",
      "Epoch 929/1000:  train Loss: 33.4967   val Loss: 33.2293   time: 1.93s   best: 33.1198\n",
      "Epoch 930/1000:  train Loss: 33.5597   val Loss: 33.1868   time: 1.94s   best: 33.1198\n",
      "Epoch 931/1000:  train Loss: 33.4651   val Loss: 33.3546   time: 1.95s   best: 33.1198\n",
      "Epoch 932/1000:  train Loss: 33.4790   val Loss: 33.1140   time: 1.95s   best: 33.1140\n",
      "Epoch 933/1000:  train Loss: 33.4103   val Loss: 33.1633   time: 1.94s   best: 33.1140\n",
      "Epoch 934/1000:  train Loss: 33.4464   val Loss: 33.2360   time: 1.94s   best: 33.1140\n",
      "Epoch 935/1000:  train Loss: 33.5089   val Loss: 33.1256   time: 1.92s   best: 33.1140\n",
      "Epoch 936/1000:  train Loss: 33.4185   val Loss: 33.0742   time: 1.92s   best: 33.0742\n",
      "Epoch 937/1000:  train Loss: 33.4230   val Loss: 33.4377   time: 1.91s   best: 33.0742\n",
      "Epoch 938/1000:  train Loss: 33.3810   val Loss: 33.3121   time: 1.91s   best: 33.0742\n",
      "Epoch 939/1000:  train Loss: 33.4341   val Loss: 33.1116   time: 1.90s   best: 33.0742\n",
      "Epoch 940/1000:  train Loss: 33.3868   val Loss: 33.1540   time: 1.90s   best: 33.0742\n",
      "Epoch 941/1000:  train Loss: 33.3496   val Loss: 33.2130   time: 1.90s   best: 33.0742\n",
      "Epoch 942/1000:  train Loss: 33.4097   val Loss: 33.6957   time: 1.92s   best: 33.0742\n",
      "Epoch 943/1000:  train Loss: 33.4172   val Loss: 33.3008   time: 1.94s   best: 33.0742\n",
      "Epoch 944/1000:  train Loss: 33.3296   val Loss: 33.2615   time: 1.94s   best: 33.0742\n",
      "Epoch 945/1000:  train Loss: 33.4078   val Loss: 33.1230   time: 1.94s   best: 33.0742\n",
      "Epoch 946/1000:  train Loss: 33.5276   val Loss: 33.2733   time: 1.94s   best: 33.0742\n",
      "Epoch 947/1000:  train Loss: 33.4673   val Loss: 33.1982   time: 1.94s   best: 33.0742\n",
      "Epoch 948/1000:  train Loss: 33.3368   val Loss: 33.1782   time: 1.93s   best: 33.0742\n",
      "Epoch 949/1000:  train Loss: 33.3125   val Loss: 33.1122   time: 1.92s   best: 33.0742\n",
      "Epoch 950/1000:  train Loss: 33.3770   val Loss: 33.1505   time: 1.91s   best: 33.0742\n",
      "Epoch 951/1000:  train Loss: 33.2734   val Loss: 33.1760   time: 1.90s   best: 33.0742\n",
      "Epoch 952/1000:  train Loss: 33.3635   val Loss: 33.2767   time: 1.90s   best: 33.0742\n",
      "Epoch 953/1000:  train Loss: 33.3071   val Loss: 33.3211   time: 1.92s   best: 33.0742\n",
      "Epoch 954/1000:  train Loss: 33.3812   val Loss: 33.3388   time: 1.92s   best: 33.0742\n",
      "Epoch 955/1000:  train Loss: 33.2930   val Loss: 33.3342   time: 1.95s   best: 33.0742\n",
      "Epoch 956/1000:  train Loss: 33.2586   val Loss: 33.2175   time: 1.95s   best: 33.0742\n",
      "Epoch 957/1000:  train Loss: 33.2055   val Loss: 33.2596   time: 1.97s   best: 33.0742\n",
      "Epoch 958/1000:  train Loss: 33.3063   val Loss: 33.2635   time: 1.95s   best: 33.0742\n",
      "Epoch 959/1000:  train Loss: 33.3285   val Loss: 33.0790   time: 1.95s   best: 33.0742\n",
      "Epoch 960/1000:  train Loss: 33.4108   val Loss: 33.4779   time: 1.95s   best: 33.0742\n",
      "Epoch 961/1000:  train Loss: 33.2369   val Loss: 33.1601   time: 1.93s   best: 33.0742\n",
      "Epoch 962/1000:  train Loss: 33.3765   val Loss: 33.3468   time: 1.92s   best: 33.0742\n",
      "Epoch 963/1000:  train Loss: 33.2124   val Loss: 33.1162   time: 1.91s   best: 33.0742\n",
      "Epoch 964/1000:  train Loss: 33.2619   val Loss: 33.1571   time: 1.90s   best: 33.0742\n",
      "Epoch 965/1000:  train Loss: 33.3201   val Loss: 33.2642   time: 1.90s   best: 33.0742\n",
      "Epoch 966/1000:  train Loss: 33.1843   val Loss: 33.0016   time: 1.90s   best: 33.0016\n",
      "Epoch 967/1000:  train Loss: 33.2736   val Loss: 33.2527   time: 1.95s   best: 33.0016\n",
      "Epoch 968/1000:  train Loss: 33.2857   val Loss: 32.9822   time: 1.97s   best: 32.9822\n",
      "Epoch 969/1000:  train Loss: 33.2241   val Loss: 33.0724   time: 1.96s   best: 32.9822\n",
      "Epoch 970/1000:  train Loss: 33.3137   val Loss: 33.2109   time: 1.94s   best: 32.9822\n",
      "Epoch 971/1000:  train Loss: 33.1276   val Loss: 33.1306   time: 1.94s   best: 32.9822\n",
      "Epoch 972/1000:  train Loss: 33.1768   val Loss: 33.1142   time: 1.94s   best: 32.9822\n",
      "Epoch 973/1000:  train Loss: 33.1561   val Loss: 33.1403   time: 1.93s   best: 32.9822\n",
      "Epoch 974/1000:  train Loss: 33.1835   val Loss: 32.9861   time: 1.92s   best: 32.9822\n",
      "Epoch 975/1000:  train Loss: 33.0230   val Loss: 33.3370   time: 1.92s   best: 32.9822\n",
      "Epoch 976/1000:  train Loss: 33.1738   val Loss: 33.0599   time: 1.91s   best: 32.9822\n",
      "Epoch 977/1000:  train Loss: 33.1230   val Loss: 33.2710   time: 1.90s   best: 32.9822\n",
      "Epoch 978/1000:  train Loss: 33.1222   val Loss: 33.1555   time: 1.90s   best: 32.9822\n",
      "Epoch 979/1000:  train Loss: 33.1243   val Loss: 33.1331   time: 1.90s   best: 32.9822\n",
      "Epoch 980/1000:  train Loss: 33.0846   val Loss: 33.0843   time: 1.92s   best: 32.9822\n",
      "Epoch 981/1000:  train Loss: 33.1331   val Loss: 33.3722   time: 1.93s   best: 32.9822\n",
      "Epoch 982/1000:  train Loss: 33.2261   val Loss: 32.9728   time: 1.94s   best: 32.9728\n",
      "Epoch 983/1000:  train Loss: 33.1442   val Loss: 33.2143   time: 1.94s   best: 32.9728\n",
      "Epoch 984/1000:  train Loss: 33.1443   val Loss: 33.1329   time: 1.94s   best: 32.9728\n",
      "Epoch 985/1000:  train Loss: 33.1876   val Loss: 33.1522   time: 1.95s   best: 32.9728\n",
      "Epoch 986/1000:  train Loss: 33.1113   val Loss: 32.9768   time: 1.93s   best: 32.9728\n",
      "Epoch 987/1000:  train Loss: 33.0498   val Loss: 33.0436   time: 1.92s   best: 32.9728\n",
      "Epoch 988/1000:  train Loss: 33.1651   val Loss: 33.1046   time: 1.92s   best: 32.9728\n",
      "Epoch 989/1000:  train Loss: 33.0172   val Loss: 33.1387   time: 1.91s   best: 32.9728\n",
      "Epoch 990/1000:  train Loss: 33.1028   val Loss: 33.2194   time: 1.90s   best: 32.9728\n",
      "Epoch 991/1000:  train Loss: 33.0912   val Loss: 33.3317   time: 1.90s   best: 32.9728\n",
      "Epoch 992/1000:  train Loss: 32.9979   val Loss: 33.3800   time: 1.90s   best: 32.9728\n",
      "Epoch 993/1000:  train Loss: 33.0493   val Loss: 33.1311   time: 1.92s   best: 32.9728\n",
      "Epoch 994/1000:  train Loss: 33.0717   val Loss: 33.0465   time: 1.94s   best: 32.9728\n",
      "Epoch 995/1000:  train Loss: 33.1019   val Loss: 33.0191   time: 1.95s   best: 32.9728\n",
      "Epoch 996/1000:  train Loss: 33.0755   val Loss: 32.9997   time: 1.94s   best: 32.9728\n",
      "Epoch 997/1000:  train Loss: 33.0084   val Loss: 33.0274   time: 1.94s   best: 32.9728\n",
      "Epoch 998/1000:  train Loss: 33.0196   val Loss: 33.1360   time: 1.94s   best: 32.9728\n",
      "Epoch 999/1000:  train Loss: 32.9618   val Loss: 33.1888   time: 1.93s   best: 32.9728\n",
      "Epoch 1000/1000:  train Loss: 33.0246   val Loss: 33.0017   time: 1.92s   best: 32.9728\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete in 32m 11s   best validation loss: 32.9728\n"
     ]
    }
   ],
   "source": [
    "bl_dict={'train':train_bl, 'val':val_bl}\n",
    "log_file = 'log_test1.txt'\n",
    "\n",
    "losses, best_model = train_model(model, criterion, optimizer , max_lr=0.001, dataloader=bl_dict,\n",
    "                                 num_epochs=1000, logFile=log_file, log_every=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(df):\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    return accuracy_score(df['target'].values.tolist(), df['prediction'].values.tolist())*100\n",
    "\n",
    "def report(df):\n",
    "    from sklearn.metrics import classification_report\n",
    "    print(classification_report(df['target'].values.tolist(), df['prediction'].values.tolist(), zero_division=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [00:00<00:00, 292.92it/s]\n",
      "100%|██████████| 31/31 [00:00<00:00, 379.77it/s]\n",
      "100%|██████████| 234/234 [00:00<00:00, 362.36it/s]\n",
      "100%|██████████| 3/3 [00:00<00:00, 81.15it/s]\n"
     ]
    }
   ],
   "source": [
    "val_df = get_results_df(train_val_dataset, val_bl, val_indices, model)\n",
    "test_df = get_results_df(test_dataset, test_bl, test_indices, model)\n",
    "train_df = get_results_df(train_val_dataset, train_bl, train_indices, model)\n",
    "benchmark_df = get_results_df(bench_dataset, bench_bl, bench_indices, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'Models/1stModel_final.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.71      0.78     99058\n",
      "           1       0.87      0.91      0.89    166814\n",
      "           2       0.90      0.94      0.92    189964\n",
      "           3       0.81      0.84      0.82     16678\n",
      "           4       0.76      0.93      0.84      1034\n",
      "           5       1.00      1.00      1.00         8\n",
      "           6       1.00      1.00      1.00         0\n",
      "           7       1.00      1.00      1.00         0\n",
      "           8       1.00      1.00      1.00         0\n",
      "           9       1.00      1.00      1.00         0\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.88      0.88      0.88    473556\n",
      "   macro avg       0.97      0.98      0.97    473556\n",
      "weighted avg       0.88      0.88      0.88    473556\n",
      " samples avg       0.88      0.88      0.88    473556\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.67      0.74     10632\n",
      "           1       0.86      0.90      0.88     18372\n",
      "           2       0.88      0.92      0.90     19802\n",
      "           3       0.78      0.80      0.79      2394\n",
      "           4       1.00      1.00      1.00         0\n",
      "           5       1.00      1.00      1.00         0\n",
      "           6       1.00      1.00      1.00         0\n",
      "           7       1.00      1.00      1.00         0\n",
      "           8       1.00      1.00      1.00         0\n",
      "           9       1.00      1.00      1.00         0\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.86      0.86      0.86     51200\n",
      "   macro avg       0.98      0.97      0.98     51200\n",
      "weighted avg       0.86      0.86      0.86     51200\n",
      " samples avg       0.86      0.86      0.86     51200\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.68      0.75     11986\n",
      "           1       0.85      0.90      0.87     20457\n",
      "           2       0.88      0.92      0.90     23349\n",
      "           3       0.76      0.77      0.76      2022\n",
      "           4       0.64      0.74      0.69       145\n",
      "           5       1.00      1.00      1.00         0\n",
      "           6       1.00      1.00      1.00         0\n",
      "           7       1.00      1.00      1.00         0\n",
      "           8       1.00      1.00      1.00         0\n",
      "           9       1.00      1.00      1.00         0\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.86      0.86      0.86     57959\n",
      "   macro avg       0.96      0.96      0.96     57959\n",
      "weighted avg       0.86      0.86      0.85     57959\n",
      " samples avg       0.86      0.86      0.86     57959\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.37      0.88      0.52        68\n",
      "           1       0.92      0.71      0.80        83\n",
      "           2       0.89      0.75      0.81       153\n",
      "           3       1.00      0.00      0.00        49\n",
      "           4       1.00      1.00      1.00         0\n",
      "           5       1.00      1.00      1.00         0\n",
      "           6       1.00      1.00      1.00         0\n",
      "           7       1.00      1.00      1.00         0\n",
      "           8       1.00      1.00      1.00         0\n",
      "           9       1.00      1.00      1.00         0\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.66      0.66      0.66       353\n",
      "   macro avg       0.97      0.94      0.93       353\n",
      "weighted avg       0.81      0.66      0.64       353\n",
      " samples avg       0.66      0.66      0.66       353\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(train_df)\n",
    "report(val_df)\n",
    "report(test_df)\n",
    "report(benchmark_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2nd model of Tiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run utils_Tiling2_RLSTM.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "<a id='load_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data from  /data/kk4796/datasets/dataset_13M_train_val.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89655/89655 [1:19:02<00:00, 18.90it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 439\n",
      "Number of batches dropped due to too much memory accesses:23259\n",
      "Data loaded\n",
      "Sizes: (43, 396) batches\n",
      "loading data from  /data/kk4796/datasets/dataset_13M_test.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9961/9961 [08:46<00:00, 18.94it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 51\n",
      "Number of batches dropped due to too much memory accesses:2301\n",
      "Data loaded\n",
      "Sizes: (51, 0) batches\n",
      "loading data from  /data/mm12191/datasets/benchmarks_ds2.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:08<00:00,  3.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 3\n",
      "Data loaded\n",
      "Sizes: (3, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_val_dataset2, val_bl2, val_indices2, train_bl2, train_indices2 = load_data(train_val_dataset_file,split_ratio=0.1, max_batch_size=2048, filter_func=filter_schedule)\n",
    "test_dataset2, test_bl2, test_indices2, _, _ = load_data(test_dataset_file,split_ratio=1, max_batch_size=2048, filter_func=filter_schedule)\n",
    "bench_dataset2, bench_bl2, bench_indices2, _, _ = load_data(benchmark_dataset_file,split_ratio=1, max_batch_size=2048, filter_func=filter_schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 1272  # size of all the vectors\n",
    "\n",
    "model2 = None \n",
    "model2 = Model_Recursive_LSTM_v2(input_size, comp_embed_layer_sizes=[600, 900, 600, 400, 200], drops=[0.4, 0.4, 0.4, 0.4, 0.4], output_size=37)\n",
    "model2.to(train_device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = AdamW(model2.parameters(),weight_decay=0.375e-2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000:  train Loss: 194.1422   val Loss: 143.1295   time: 3.26s   best: 143.1295\n",
      "Epoch 2/1000:  train Loss: 141.3739   val Loss: 128.7233   time: 3.27s   best: 128.7233\n",
      "Epoch 3/1000:  train Loss: 130.1529   val Loss: 123.0331   time: 3.37s   best: 123.0331\n",
      "Epoch 4/1000:  train Loss: 126.0590   val Loss: 121.5166   time: 3.35s   best: 121.5166\n",
      "Epoch 5/1000:  train Loss: 124.1078   val Loss: 120.7681   time: 3.30s   best: 120.7681\n",
      "Epoch 6/1000:  train Loss: 122.8439   val Loss: 120.2498   time: 3.26s   best: 120.2498\n",
      "Epoch 7/1000:  train Loss: 121.8940   val Loss: 119.7816   time: 3.26s   best: 119.7816\n",
      "Epoch 8/1000:  train Loss: 121.0365   val Loss: 119.4886   time: 3.25s   best: 119.4886\n",
      "Epoch 9/1000:  train Loss: 120.4734   val Loss: 119.2723   time: 3.25s   best: 119.2723\n",
      "Epoch 10/1000:  train Loss: 119.8922   val Loss: 118.9013   time: 3.29s   best: 118.9013\n",
      "Epoch 11/1000:  train Loss: 119.3966   val Loss: 118.8237   time: 3.37s   best: 118.8237\n",
      "Epoch 12/1000:  train Loss: 118.9936   val Loss: 118.6270   time: 3.36s   best: 118.6270\n",
      "Epoch 13/1000:  train Loss: 118.5858   val Loss: 118.1802   time: 3.35s   best: 118.1802\n",
      "Epoch 14/1000:  train Loss: 118.1909   val Loss: 117.9455   time: 3.42s   best: 117.9455\n",
      "Epoch 15/1000:  train Loss: 117.8487   val Loss: 117.5314   time: 3.40s   best: 117.5314\n",
      "Epoch 16/1000:  train Loss: 117.5094   val Loss: 117.3052   time: 3.40s   best: 117.3052\n",
      "Epoch 17/1000:  train Loss: 117.1290   val Loss: 117.0396   time: 3.41s   best: 117.0396\n",
      "Epoch 18/1000:  train Loss: 116.7551   val Loss: 116.6957   time: 3.53s   best: 116.6957\n",
      "Epoch 19/1000:  train Loss: 116.4354   val Loss: 116.3402   time: 3.52s   best: 116.3402\n",
      "Epoch 20/1000:  train Loss: 116.0902   val Loss: 116.2578   time: 3.46s   best: 116.2578\n",
      "Epoch 21/1000:  train Loss: 115.7626   val Loss: 116.1443   time: 3.41s   best: 116.1443\n",
      "Epoch 22/1000:  train Loss: 115.5677   val Loss: 115.9134   time: 3.41s   best: 115.9134\n",
      "Epoch 23/1000:  train Loss: 115.2997   val Loss: 115.6259   time: 3.40s   best: 115.6259\n",
      "Epoch 24/1000:  train Loss: 115.0881   val Loss: 115.5884   time: 3.40s   best: 115.5884\n",
      "Epoch 25/1000:  train Loss: 114.8634   val Loss: 115.3153   time: 3.50s   best: 115.3153\n",
      "Epoch 26/1000:  train Loss: 114.6522   val Loss: 115.0431   time: 3.54s   best: 115.0431\n",
      "Epoch 27/1000:  train Loss: 114.4113   val Loss: 114.9162   time: 3.47s   best: 114.9162\n",
      "Epoch 28/1000:  train Loss: 114.1679   val Loss: 114.4824   time: 3.42s   best: 114.4824\n",
      "Epoch 29/1000:  train Loss: 113.9839   val Loss: 114.1776   time: 3.41s   best: 114.1776\n",
      "Epoch 30/1000:  train Loss: 113.7869   val Loss: 113.9289   time: 3.40s   best: 113.9289\n",
      "Epoch 31/1000:  train Loss: 113.5019   val Loss: 113.6321   time: 3.40s   best: 113.6321\n",
      "Epoch 32/1000:  train Loss: 113.3131   val Loss: 113.3014   time: 3.50s   best: 113.3014\n",
      "Epoch 33/1000:  train Loss: 113.0417   val Loss: 113.0069   time: 3.53s   best: 113.0069\n",
      "Epoch 34/1000:  train Loss: 112.8500   val Loss: 112.7497   time: 3.48s   best: 112.7497\n",
      "Epoch 35/1000:  train Loss: 112.6680   val Loss: 112.4231   time: 3.43s   best: 112.4231\n",
      "Epoch 36/1000:  train Loss: 112.4034   val Loss: 112.2926   time: 3.41s   best: 112.2926\n",
      "Epoch 37/1000:  train Loss: 112.2301   val Loss: 111.9406   time: 3.40s   best: 111.9406\n",
      "Epoch 38/1000:  train Loss: 111.9601   val Loss: 111.5457   time: 3.40s   best: 111.5457\n",
      "Epoch 39/1000:  train Loss: 111.7044   val Loss: 111.1979   time: 3.46s   best: 111.1979\n",
      "Epoch 40/1000:  train Loss: 111.4769   val Loss: 110.8927   time: 3.54s   best: 110.8927\n",
      "Epoch 41/1000:  train Loss: 111.2401   val Loss: 110.6104   time: 3.51s   best: 110.6104\n",
      "Epoch 42/1000:  train Loss: 111.0524   val Loss: 110.2985   time: 3.44s   best: 110.2985\n",
      "Epoch 43/1000:  train Loss: 110.6954   val Loss: 109.9696   time: 3.42s   best: 109.9696\n",
      "Epoch 44/1000:  train Loss: 110.3627   val Loss: 109.3800   time: 3.41s   best: 109.3800\n",
      "Epoch 45/1000:  train Loss: 109.9649   val Loss: 109.0210   time: 3.40s   best: 109.0210\n",
      "Epoch 46/1000:  train Loss: 109.3946   val Loss: 108.0101   time: 3.43s   best: 108.0101\n",
      "Epoch 47/1000:  train Loss: 108.8379   val Loss: 107.0906   time: 3.53s   best: 107.0906\n",
      "Epoch 48/1000:  train Loss: 108.1688   val Loss: 106.0604   time: 3.52s   best: 106.0604\n",
      "Epoch 49/1000:  train Loss: 107.3415   val Loss: 105.1859   time: 3.45s   best: 105.1859\n",
      "Epoch 50/1000:  train Loss: 106.5714   val Loss: 104.1248   time: 3.42s   best: 104.1248\n",
      "Epoch 51/1000:  train Loss: 105.6314   val Loss: 103.0107   time: 3.40s   best: 103.0107\n",
      "Epoch 52/1000:  train Loss: 104.8743   val Loss: 101.7815   time: 3.41s   best: 101.7815\n",
      "Epoch 53/1000:  train Loss: 104.1707   val Loss: 101.1913   time: 3.42s   best: 101.1913\n",
      "Epoch 54/1000:  train Loss: 103.5505   val Loss: 100.4831   time: 3.54s   best: 100.4831\n",
      "Epoch 55/1000:  train Loss: 103.0044   val Loss: 99.6199   time: 3.52s   best: 99.6199\n",
      "Epoch 56/1000:  train Loss: 102.3861   val Loss: 99.2554   time: 3.45s   best: 99.2554\n",
      "Epoch 57/1000:  train Loss: 101.8748   val Loss: 98.4164   time: 3.41s   best: 98.4164\n",
      "Epoch 58/1000:  train Loss: 101.3564   val Loss: 97.9353   time: 3.40s   best: 97.9353\n",
      "Epoch 59/1000:  train Loss: 100.8757   val Loss: 97.1820   time: 3.39s   best: 97.1820\n",
      "Epoch 60/1000:  train Loss: 100.4602   val Loss: 96.9460   time: 3.41s   best: 96.9460\n",
      "Epoch 61/1000:  train Loss: 100.0201   val Loss: 96.3775   time: 3.51s   best: 96.3775\n",
      "Epoch 62/1000:  train Loss: 99.5367   val Loss: 95.8123   time: 3.55s   best: 95.8123\n",
      "Epoch 63/1000:  train Loss: 99.2101   val Loss: 95.3946   time: 3.48s   best: 95.3946\n",
      "Epoch 64/1000:  train Loss: 98.7936   val Loss: 95.0928   time: 3.44s   best: 95.0928\n",
      "Epoch 65/1000:  train Loss: 98.4973   val Loss: 94.6971   time: 3.42s   best: 94.6971\n",
      "Epoch 66/1000:  train Loss: 98.1894   val Loss: 94.3110   time: 3.41s   best: 94.3110\n",
      "Epoch 67/1000:  train Loss: 97.8986   val Loss: 94.0354   time: 3.41s   best: 94.0354\n",
      "Epoch 68/1000:  train Loss: 97.5717   val Loss: 93.7038   time: 3.49s   best: 93.7038\n",
      "Epoch 69/1000:  train Loss: 97.2705   val Loss: 93.3966   time: 3.55s   best: 93.3966\n",
      "Epoch 70/1000:  train Loss: 96.9391   val Loss: 93.3174   time: 3.51s   best: 93.3174\n",
      "Epoch 71/1000:  train Loss: 96.7632   val Loss: 92.9386   time: 3.44s   best: 92.9386\n",
      "Epoch 72/1000:  train Loss: 96.4875   val Loss: 92.6648   time: 3.44s   best: 92.6648\n",
      "Epoch 73/1000:  train Loss: 96.2981   val Loss: 92.5875   time: 3.41s   best: 92.5875\n",
      "Epoch 74/1000:  train Loss: 96.0045   val Loss: 92.1810   time: 3.42s   best: 92.1810\n",
      "Epoch 75/1000:  train Loss: 95.7733   val Loss: 92.1259   time: 3.48s   best: 92.1259\n",
      "Epoch 76/1000:  train Loss: 95.5724   val Loss: 91.9227   time: 3.56s   best: 91.9227\n",
      "Epoch 77/1000:  train Loss: 95.3290   val Loss: 91.6665   time: 3.51s   best: 91.6665\n",
      "Epoch 78/1000:  train Loss: 95.1721   val Loss: 91.4949   time: 3.44s   best: 91.4949\n",
      "Epoch 79/1000:  train Loss: 95.0142   val Loss: 91.2908   time: 3.44s   best: 91.2908\n",
      "Epoch 80/1000:  train Loss: 94.8425   val Loss: 91.1415   time: 3.42s   best: 91.1415\n",
      "Epoch 81/1000:  train Loss: 94.6259   val Loss: 90.9467   time: 3.42s   best: 90.9467\n",
      "Epoch 82/1000:  train Loss: 94.4607   val Loss: 90.7682   time: 3.46s   best: 90.7682\n",
      "Epoch 83/1000:  train Loss: 94.2586   val Loss: 90.6503   time: 3.56s   best: 90.6503\n",
      "Epoch 84/1000:  train Loss: 94.1172   val Loss: 90.5496   time: 3.53s   best: 90.5496\n",
      "Epoch 85/1000:  train Loss: 93.9514   val Loss: 90.3703   time: 3.45s   best: 90.3703\n",
      "Epoch 86/1000:  train Loss: 93.8070   val Loss: 90.2083   time: 3.44s   best: 90.2083\n",
      "Epoch 87/1000:  train Loss: 93.5191   val Loss: 90.0615   time: 3.41s   best: 90.0615\n",
      "Epoch 88/1000:  train Loss: 93.4474   val Loss: 89.9417   time: 3.42s   best: 89.9417\n",
      "Epoch 89/1000:  train Loss: 93.3275   val Loss: 89.8008   time: 3.45s   best: 89.8008\n",
      "Epoch 90/1000:  train Loss: 93.2024   val Loss: 89.6690   time: 3.56s   best: 89.6690\n",
      "Epoch 91/1000:  train Loss: 93.0714   val Loss: 89.5835   time: 3.52s   best: 89.5835\n",
      "Epoch 92/1000:  train Loss: 92.9434   val Loss: 89.4486   time: 3.46s   best: 89.4486\n",
      "Epoch 93/1000:  train Loss: 92.7671   val Loss: 89.4229   time: 3.44s   best: 89.4229\n",
      "Epoch 94/1000:  train Loss: 92.6718   val Loss: 89.2395   time: 3.42s   best: 89.2395\n",
      "Epoch 95/1000:  train Loss: 92.5499   val Loss: 89.1375   time: 3.41s   best: 89.1375\n",
      "Epoch 96/1000:  train Loss: 92.4846   val Loss: 89.2343   time: 3.44s   best: 89.1375\n",
      "Epoch 97/1000:  train Loss: 92.3721   val Loss: 89.0192   time: 3.57s   best: 89.0192\n",
      "Epoch 98/1000:  train Loss: 92.1558   val Loss: 88.8067   time: 3.54s   best: 88.8067\n",
      "Epoch 99/1000:  train Loss: 92.0701   val Loss: 88.7093   time: 3.46s   best: 88.7093\n",
      "Epoch 100/1000:  train Loss: 91.9509   val Loss: 88.6505   time: 3.43s   best: 88.6505\n",
      "Epoch 101/1000:  train Loss: 91.8486   val Loss: 88.5045   time: 3.42s   best: 88.5045\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/1000:  train Loss: 91.6910   val Loss: 88.3907   time: 3.42s   best: 88.3907\n",
      "Epoch 103/1000:  train Loss: 91.6169   val Loss: 88.3274   time: 3.44s   best: 88.3274\n",
      "Epoch 104/1000:  train Loss: 91.5155   val Loss: 88.1581   time: 3.55s   best: 88.1581\n",
      "Epoch 105/1000:  train Loss: 91.4002   val Loss: 88.0934   time: 3.55s   best: 88.0934\n",
      "Epoch 106/1000:  train Loss: 91.2976   val Loss: 88.0842   time: 3.44s   best: 88.0842\n",
      "Epoch 107/1000:  train Loss: 91.2086   val Loss: 87.9314   time: 3.43s   best: 87.9314\n",
      "Epoch 108/1000:  train Loss: 91.1317   val Loss: 87.9340   time: 3.43s   best: 87.9314\n",
      "Epoch 109/1000:  train Loss: 90.9061   val Loss: 87.7967   time: 3.41s   best: 87.7967\n",
      "Epoch 110/1000:  train Loss: 90.9106   val Loss: 87.6000   time: 3.42s   best: 87.6000\n",
      "Epoch 111/1000:  train Loss: 90.7859   val Loss: 87.7542   time: 3.52s   best: 87.6000\n",
      "Epoch 112/1000:  train Loss: 90.6883   val Loss: 87.4763   time: 3.56s   best: 87.4763\n",
      "Epoch 113/1000:  train Loss: 90.5798   val Loss: 87.4214   time: 3.49s   best: 87.4214\n",
      "Epoch 114/1000:  train Loss: 90.5090   val Loss: 87.3911   time: 3.43s   best: 87.3911\n",
      "Epoch 115/1000:  train Loss: 90.4318   val Loss: 87.2827   time: 3.43s   best: 87.2827\n",
      "Epoch 116/1000:  train Loss: 90.2599   val Loss: 87.0857   time: 3.41s   best: 87.0857\n",
      "Epoch 117/1000:  train Loss: 90.1776   val Loss: 87.0400   time: 3.42s   best: 87.0400\n",
      "Epoch 118/1000:  train Loss: 90.2051   val Loss: 87.0459   time: 3.48s   best: 87.0400\n",
      "Epoch 119/1000:  train Loss: 89.9984   val Loss: 86.8832   time: 3.56s   best: 86.8832\n",
      "Epoch 120/1000:  train Loss: 89.9711   val Loss: 86.8430   time: 3.52s   best: 86.8430\n",
      "Epoch 121/1000:  train Loss: 89.9101   val Loss: 86.7677   time: 3.43s   best: 86.7677\n",
      "Epoch 122/1000:  train Loss: 89.7965   val Loss: 86.7835   time: 3.43s   best: 86.7677\n",
      "Epoch 123/1000:  train Loss: 89.7329   val Loss: 86.6391   time: 3.41s   best: 86.6391\n",
      "Epoch 124/1000:  train Loss: 89.6371   val Loss: 86.5282   time: 3.40s   best: 86.5282\n",
      "Epoch 125/1000:  train Loss: 89.5996   val Loss: 86.4814   time: 3.44s   best: 86.4814\n",
      "Epoch 126/1000:  train Loss: 89.4276   val Loss: 86.4079   time: 3.55s   best: 86.4079\n",
      "Epoch 127/1000:  train Loss: 89.4446   val Loss: 86.2871   time: 3.53s   best: 86.2871\n",
      "Epoch 128/1000:  train Loss: 89.3490   val Loss: 86.4039   time: 3.43s   best: 86.2871\n",
      "Epoch 129/1000:  train Loss: 89.2625   val Loss: 86.2364   time: 3.42s   best: 86.2364\n",
      "Epoch 130/1000:  train Loss: 89.1832   val Loss: 86.1120   time: 3.41s   best: 86.1120\n",
      "Epoch 131/1000:  train Loss: 89.1002   val Loss: 86.0692   time: 3.40s   best: 86.0692\n",
      "Epoch 132/1000:  train Loss: 89.0041   val Loss: 86.0205   time: 3.41s   best: 86.0205\n",
      "Epoch 133/1000:  train Loss: 88.9593   val Loss: 86.0650   time: 3.52s   best: 86.0205\n",
      "Epoch 134/1000:  train Loss: 88.9035   val Loss: 85.8290   time: 3.54s   best: 85.8290\n",
      "Epoch 135/1000:  train Loss: 88.7359   val Loss: 85.8019   time: 3.45s   best: 85.8019\n",
      "Epoch 136/1000:  train Loss: 88.7125   val Loss: 85.7671   time: 3.42s   best: 85.7671\n",
      "Epoch 137/1000:  train Loss: 88.6779   val Loss: 85.6749   time: 3.41s   best: 85.6749\n",
      "Epoch 138/1000:  train Loss: 88.6470   val Loss: 85.5812   time: 3.40s   best: 85.5812\n",
      "Epoch 139/1000:  train Loss: 88.5257   val Loss: 85.5643   time: 3.41s   best: 85.5643\n",
      "Epoch 140/1000:  train Loss: 88.4518   val Loss: 85.5597   time: 3.51s   best: 85.5597\n",
      "Epoch 141/1000:  train Loss: 88.4414   val Loss: 85.4363   time: 3.55s   best: 85.4363\n",
      "Epoch 142/1000:  train Loss: 88.3856   val Loss: 85.5449   time: 3.47s   best: 85.4363\n",
      "Epoch 143/1000:  train Loss: 88.3348   val Loss: 85.4691   time: 3.42s   best: 85.4363\n",
      "Epoch 144/1000:  train Loss: 88.2303   val Loss: 85.2769   time: 3.41s   best: 85.2769\n",
      "Epoch 145/1000:  train Loss: 88.1822   val Loss: 85.2662   time: 3.40s   best: 85.2662\n",
      "Epoch 146/1000:  train Loss: 88.1106   val Loss: 85.2095   time: 3.42s   best: 85.2095\n",
      "Epoch 147/1000:  train Loss: 88.0301   val Loss: 85.1232   time: 3.48s   best: 85.1232\n",
      "Epoch 148/1000:  train Loss: 87.9765   val Loss: 85.1549   time: 3.57s   best: 85.1232\n",
      "Epoch 149/1000:  train Loss: 87.9439   val Loss: 85.0459   time: 3.52s   best: 85.0459\n",
      "Epoch 150/1000:  train Loss: 87.7987   val Loss: 84.9530   time: 3.43s   best: 84.9530\n",
      "Epoch 151/1000:  train Loss: 87.7747   val Loss: 84.9473   time: 3.43s   best: 84.9473\n",
      "Epoch 152/1000:  train Loss: 87.7562   val Loss: 84.8989   time: 3.42s   best: 84.8989\n",
      "Epoch 153/1000:  train Loss: 87.6639   val Loss: 84.8134   time: 3.41s   best: 84.8134\n",
      "Epoch 154/1000:  train Loss: 87.5764   val Loss: 84.7972   time: 3.46s   best: 84.7972\n",
      "Epoch 155/1000:  train Loss: 87.5296   val Loss: 84.6793   time: 3.57s   best: 84.6793\n",
      "Epoch 156/1000:  train Loss: 87.4925   val Loss: 84.7291   time: 3.53s   best: 84.6793\n",
      "Epoch 157/1000:  train Loss: 87.5106   val Loss: 84.5690   time: 3.43s   best: 84.5690\n",
      "Epoch 158/1000:  train Loss: 87.4100   val Loss: 84.5753   time: 3.44s   best: 84.5690\n",
      "Epoch 159/1000:  train Loss: 87.3798   val Loss: 84.4637   time: 3.42s   best: 84.4637\n",
      "Epoch 160/1000:  train Loss: 87.2341   val Loss: 84.4381   time: 3.42s   best: 84.4381\n",
      "Epoch 161/1000:  train Loss: 87.2457   val Loss: 84.3781   time: 3.44s   best: 84.3781\n",
      "Epoch 162/1000:  train Loss: 87.2130   val Loss: 84.3109   time: 3.56s   best: 84.3109\n",
      "Epoch 163/1000:  train Loss: 87.1659   val Loss: 84.2159   time: 3.54s   best: 84.2159\n",
      "Epoch 164/1000:  train Loss: 87.0510   val Loss: 84.1576   time: 3.45s   best: 84.1576\n",
      "Epoch 165/1000:  train Loss: 86.9615   val Loss: 84.1653   time: 3.44s   best: 84.1576\n",
      "Epoch 166/1000:  train Loss: 86.8966   val Loss: 84.1464   time: 3.42s   best: 84.1464\n",
      "Epoch 167/1000:  train Loss: 86.9890   val Loss: 84.1009   time: 3.42s   best: 84.1009\n",
      "Epoch 168/1000:  train Loss: 86.9091   val Loss: 84.0509   time: 3.42s   best: 84.0509\n",
      "Epoch 169/1000:  train Loss: 86.7918   val Loss: 83.9620   time: 3.54s   best: 83.9620\n",
      "Epoch 170/1000:  train Loss: 86.7870   val Loss: 83.9506   time: 3.55s   best: 83.9506\n",
      "Epoch 171/1000:  train Loss: 86.7075   val Loss: 83.9338   time: 3.47s   best: 83.9338\n",
      "Epoch 172/1000:  train Loss: 86.6327   val Loss: 83.8303   time: 3.43s   best: 83.8303\n",
      "Epoch 173/1000:  train Loss: 86.5968   val Loss: 83.8737   time: 3.42s   best: 83.8303\n",
      "Epoch 174/1000:  train Loss: 86.7469   val Loss: 83.8582   time: 3.41s   best: 83.8303\n",
      "Epoch 175/1000:  train Loss: 86.5652   val Loss: 83.7551   time: 3.41s   best: 83.7551\n",
      "Epoch 176/1000:  train Loss: 86.4687   val Loss: 83.7565   time: 3.50s   best: 83.7551\n",
      "Epoch 177/1000:  train Loss: 86.4357   val Loss: 83.7740   time: 3.55s   best: 83.7551\n",
      "Epoch 178/1000:  train Loss: 86.3768   val Loss: 83.6125   time: 3.48s   best: 83.6125\n",
      "Epoch 179/1000:  train Loss: 86.3662   val Loss: 83.6215   time: 3.42s   best: 83.6125\n",
      "Epoch 180/1000:  train Loss: 86.3204   val Loss: 83.5052   time: 3.42s   best: 83.5052\n",
      "Epoch 181/1000:  train Loss: 86.2754   val Loss: 83.4567   time: 3.40s   best: 83.4567\n",
      "Epoch 182/1000:  train Loss: 86.2535   val Loss: 83.4656   time: 3.41s   best: 83.4567\n",
      "Epoch 183/1000:  train Loss: 86.2872   val Loss: 83.4619   time: 3.49s   best: 83.4567\n",
      "Epoch 184/1000:  train Loss: 86.2012   val Loss: 83.5111   time: 3.56s   best: 83.4567\n",
      "Epoch 185/1000:  train Loss: 86.1470   val Loss: 83.3074   time: 3.49s   best: 83.3074\n",
      "Epoch 186/1000:  train Loss: 86.0000   val Loss: 83.2573   time: 3.42s   best: 83.2573\n",
      "Epoch 187/1000:  train Loss: 86.0491   val Loss: 83.3048   time: 3.42s   best: 83.2573\n",
      "Epoch 188/1000:  train Loss: 85.9765   val Loss: 83.1782   time: 3.41s   best: 83.1782\n",
      "Epoch 189/1000:  train Loss: 85.9306   val Loss: 83.1673   time: 3.40s   best: 83.1673\n",
      "Epoch 190/1000:  train Loss: 85.9145   val Loss: 83.1825   time: 3.45s   best: 83.1673\n",
      "Epoch 191/1000:  train Loss: 85.8496   val Loss: 83.0604   time: 3.56s   best: 83.0604\n",
      "Epoch 192/1000:  train Loss: 85.8003   val Loss: 83.0403   time: 3.52s   best: 83.0403\n",
      "Epoch 193/1000:  train Loss: 85.7276   val Loss: 83.0007   time: 3.43s   best: 83.0007\n",
      "Epoch 194/1000:  train Loss: 85.6970   val Loss: 82.9879   time: 3.43s   best: 82.9879\n",
      "Epoch 195/1000:  train Loss: 85.6449   val Loss: 82.9254   time: 3.42s   best: 82.9254\n",
      "Epoch 196/1000:  train Loss: 85.5821   val Loss: 82.8796   time: 3.42s   best: 82.8796\n",
      "Epoch 197/1000:  train Loss: 85.6253   val Loss: 83.1046   time: 3.45s   best: 82.8796\n",
      "Epoch 198/1000:  train Loss: 85.5521   val Loss: 82.8317   time: 3.56s   best: 82.8317\n",
      "Epoch 199/1000:  train Loss: 85.5940   val Loss: 82.7537   time: 3.55s   best: 82.7537\n",
      "Epoch 200/1000:  train Loss: 85.5091   val Loss: 82.8982   time: 3.45s   best: 82.7537\n",
      "Epoch 201/1000:  train Loss: 85.4346   val Loss: 82.8793   time: 3.44s   best: 82.7537\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202/1000:  train Loss: 85.5043   val Loss: 82.7853   time: 3.43s   best: 82.7537\n",
      "Epoch 203/1000:  train Loss: 85.3689   val Loss: 82.7634   time: 3.42s   best: 82.7537\n",
      "Epoch 204/1000:  train Loss: 85.3488   val Loss: 82.7102   time: 3.43s   best: 82.7102\n",
      "Epoch 205/1000:  train Loss: 85.2960   val Loss: 82.6031   time: 3.54s   best: 82.6031\n",
      "Epoch 206/1000:  train Loss: 85.2652   val Loss: 82.6123   time: 3.56s   best: 82.6031\n",
      "Epoch 207/1000:  train Loss: 85.2921   val Loss: 82.7435   time: 3.47s   best: 82.6031\n",
      "Epoch 208/1000:  train Loss: 85.2700   val Loss: 82.6105   time: 3.44s   best: 82.6031\n",
      "Epoch 209/1000:  train Loss: 85.2507   val Loss: 82.5869   time: 3.44s   best: 82.5869\n",
      "Epoch 210/1000:  train Loss: 85.1717   val Loss: 82.5820   time: 3.42s   best: 82.5820\n",
      "Epoch 211/1000:  train Loss: 85.2128   val Loss: 82.5729   time: 3.42s   best: 82.5729\n",
      "Epoch 212/1000:  train Loss: 85.1900   val Loss: 82.4662   time: 3.48s   best: 82.4662\n",
      "Epoch 213/1000:  train Loss: 85.0983   val Loss: 82.3694   time: 3.58s   best: 82.3694\n",
      "Epoch 214/1000:  train Loss: 85.0921   val Loss: 82.4124   time: 3.52s   best: 82.3694\n",
      "Epoch 215/1000:  train Loss: 84.9724   val Loss: 82.3807   time: 3.44s   best: 82.3694\n",
      "Epoch 216/1000:  train Loss: 84.9963   val Loss: 82.3667   time: 3.44s   best: 82.3667\n",
      "Epoch 217/1000:  train Loss: 84.8650   val Loss: 82.2408   time: 3.42s   best: 82.2408\n",
      "Epoch 218/1000:  train Loss: 84.7898   val Loss: 82.1995   time: 3.42s   best: 82.1995\n",
      "Epoch 219/1000:  train Loss: 84.8935   val Loss: 82.2176   time: 3.46s   best: 82.1995\n",
      "Epoch 220/1000:  train Loss: 84.7799   val Loss: 82.1565   time: 3.58s   best: 82.1565\n",
      "Epoch 221/1000:  train Loss: 84.7726   val Loss: 82.1620   time: 3.53s   best: 82.1565\n",
      "Epoch 222/1000:  train Loss: 84.7923   val Loss: 82.1249   time: 3.44s   best: 82.1249\n",
      "Epoch 223/1000:  train Loss: 84.7595   val Loss: 82.2340   time: 3.43s   best: 82.1249\n",
      "Epoch 224/1000:  train Loss: 84.6965   val Loss: 82.1049   time: 3.41s   best: 82.1049\n",
      "Epoch 225/1000:  train Loss: 84.6200   val Loss: 82.0577   time: 3.40s   best: 82.0577\n",
      "Epoch 226/1000:  train Loss: 84.6230   val Loss: 82.0804   time: 3.42s   best: 82.0577\n",
      "Epoch 227/1000:  train Loss: 84.6423   val Loss: 82.0014   time: 3.54s   best: 82.0014\n",
      "Epoch 228/1000:  train Loss: 84.7881   val Loss: 81.9621   time: 3.54s   best: 81.9621\n",
      "Epoch 229/1000:  train Loss: 84.5929   val Loss: 81.9457   time: 3.44s   best: 81.9457\n",
      "Epoch 230/1000:  train Loss: 84.5159   val Loss: 81.9239   time: 3.42s   best: 81.9239\n",
      "Epoch 231/1000:  train Loss: 84.4605   val Loss: 81.8869   time: 3.42s   best: 81.8869\n",
      "Epoch 232/1000:  train Loss: 84.3771   val Loss: 81.9034   time: 3.40s   best: 81.8869\n",
      "Epoch 233/1000:  train Loss: 84.3813   val Loss: 81.9751   time: 3.40s   best: 81.8869\n",
      "Epoch 234/1000:  train Loss: 84.3541   val Loss: 81.7700   time: 3.49s   best: 81.7700\n",
      "Epoch 235/1000:  train Loss: 84.3329   val Loss: 81.8349   time: 3.55s   best: 81.7700\n",
      "Epoch 236/1000:  train Loss: 84.2951   val Loss: 81.7467   time: 3.49s   best: 81.7467\n",
      "Epoch 237/1000:  train Loss: 84.2528   val Loss: 81.6718   time: 3.42s   best: 81.6718\n",
      "Epoch 238/1000:  train Loss: 84.3464   val Loss: 81.6242   time: 3.42s   best: 81.6242\n",
      "Epoch 239/1000:  train Loss: 84.3360   val Loss: 81.7074   time: 3.40s   best: 81.6242\n",
      "Epoch 240/1000:  train Loss: 84.2025   val Loss: 81.6236   time: 3.40s   best: 81.6236\n",
      "Epoch 241/1000:  train Loss: 84.2182   val Loss: 81.5761   time: 3.45s   best: 81.5761\n",
      "Epoch 242/1000:  train Loss: 84.1787   val Loss: 81.6079   time: 3.56s   best: 81.5761\n",
      "Epoch 243/1000:  train Loss: 84.1058   val Loss: 81.5576   time: 3.53s   best: 81.5576\n",
      "Epoch 244/1000:  train Loss: 84.0626   val Loss: 81.5265   time: 3.44s   best: 81.5265\n",
      "Epoch 245/1000:  train Loss: 84.0626   val Loss: 81.4372   time: 3.43s   best: 81.4372\n",
      "Epoch 246/1000:  train Loss: 83.9363   val Loss: 81.3960   time: 3.41s   best: 81.3960\n",
      "Epoch 247/1000:  train Loss: 83.9621   val Loss: 81.3513   time: 3.41s   best: 81.3513\n",
      "Epoch 248/1000:  train Loss: 83.9167   val Loss: 81.3792   time: 3.43s   best: 81.3513\n",
      "Epoch 249/1000:  train Loss: 83.8540   val Loss: 81.3696   time: 3.55s   best: 81.3513\n",
      "Epoch 250/1000:  train Loss: 83.9188   val Loss: 81.3466   time: 3.54s   best: 81.3466\n",
      "Epoch 251/1000:  train Loss: 83.7983   val Loss: 81.3595   time: 3.44s   best: 81.3466\n",
      "Epoch 252/1000:  train Loss: 83.7893   val Loss: 81.2621   time: 3.43s   best: 81.2621\n",
      "Epoch 253/1000:  train Loss: 83.7490   val Loss: 81.3306   time: 3.42s   best: 81.2621\n",
      "Epoch 254/1000:  train Loss: 83.7730   val Loss: 81.2276   time: 3.41s   best: 81.2276\n",
      "Epoch 255/1000:  train Loss: 83.7780   val Loss: 81.2154   time: 3.41s   best: 81.2154\n",
      "Epoch 256/1000:  train Loss: 83.6930   val Loss: 81.1528   time: 3.52s   best: 81.1528\n",
      "Epoch 257/1000:  train Loss: 83.6324   val Loss: 81.2243   time: 3.55s   best: 81.1528\n",
      "Epoch 258/1000:  train Loss: 83.6019   val Loss: 81.1357   time: 3.47s   best: 81.1357\n",
      "Epoch 259/1000:  train Loss: 83.5974   val Loss: 81.1230   time: 3.43s   best: 81.1230\n",
      "Epoch 260/1000:  train Loss: 83.6204   val Loss: 81.0832   time: 3.42s   best: 81.0832\n",
      "Epoch 261/1000:  train Loss: 83.6346   val Loss: 81.0740   time: 3.41s   best: 81.0740\n",
      "Epoch 262/1000:  train Loss: 83.5400   val Loss: 81.0225   time: 3.41s   best: 81.0225\n",
      "Epoch 263/1000:  train Loss: 83.4849   val Loss: 81.0570   time: 3.48s   best: 81.0225\n",
      "Epoch 264/1000:  train Loss: 83.4312   val Loss: 80.9912   time: 3.56s   best: 80.9912\n",
      "Epoch 265/1000:  train Loss: 83.3988   val Loss: 80.9778   time: 3.49s   best: 80.9778\n",
      "Epoch 266/1000:  train Loss: 83.4500   val Loss: 80.9645   time: 3.42s   best: 80.9645\n",
      "Epoch 267/1000:  train Loss: 83.4117   val Loss: 80.9718   time: 3.42s   best: 80.9645\n",
      "Epoch 268/1000:  train Loss: 83.3956   val Loss: 81.0164   time: 3.41s   best: 80.9645\n",
      "Epoch 269/1000:  train Loss: 83.3543   val Loss: 80.8724   time: 3.42s   best: 80.8724\n",
      "Epoch 270/1000:  train Loss: 83.3379   val Loss: 80.8913   time: 3.47s   best: 80.8724\n",
      "Epoch 271/1000:  train Loss: 83.2239   val Loss: 80.8293   time: 3.58s   best: 80.8293\n",
      "Epoch 272/1000:  train Loss: 83.1941   val Loss: 80.7277   time: 3.53s   best: 80.7277\n",
      "Epoch 273/1000:  train Loss: 83.2235   val Loss: 80.7933   time: 3.44s   best: 80.7277\n",
      "Epoch 274/1000:  train Loss: 83.2318   val Loss: 80.7207   time: 3.44s   best: 80.7207\n",
      "Epoch 275/1000:  train Loss: 83.2854   val Loss: 80.7466   time: 3.42s   best: 80.7207\n",
      "Epoch 276/1000:  train Loss: 83.2098   val Loss: 80.7011   time: 3.42s   best: 80.7011\n",
      "Epoch 277/1000:  train Loss: 83.1722   val Loss: 80.6690   time: 3.44s   best: 80.6690\n",
      "Epoch 278/1000:  train Loss: 83.0914   val Loss: 80.6786   time: 3.56s   best: 80.6690\n",
      "Epoch 279/1000:  train Loss: 83.0553   val Loss: 80.5985   time: 3.55s   best: 80.5985\n",
      "Epoch 280/1000:  train Loss: 83.0229   val Loss: 80.5435   time: 3.46s   best: 80.5435\n",
      "Epoch 281/1000:  train Loss: 82.9813   val Loss: 80.5098   time: 3.44s   best: 80.5098\n",
      "Epoch 282/1000:  train Loss: 82.9509   val Loss: 80.4974   time: 3.43s   best: 80.4974\n",
      "Epoch 283/1000:  train Loss: 82.9099   val Loss: 80.4776   time: 3.42s   best: 80.4776\n",
      "Epoch 284/1000:  train Loss: 82.9192   val Loss: 80.4170   time: 3.43s   best: 80.4170\n",
      "Epoch 285/1000:  train Loss: 82.8820   val Loss: 80.3489   time: 3.53s   best: 80.3489\n",
      "Epoch 286/1000:  train Loss: 82.8582   val Loss: 80.4287   time: 3.57s   best: 80.3489\n",
      "Epoch 287/1000:  train Loss: 82.7914   val Loss: 80.3294   time: 3.48s   best: 80.3294\n",
      "Epoch 288/1000:  train Loss: 82.7804   val Loss: 80.3101   time: 3.44s   best: 80.3101\n",
      "Epoch 289/1000:  train Loss: 82.7926   val Loss: 80.1937   time: 3.43s   best: 80.1937\n",
      "Epoch 290/1000:  train Loss: 82.6979   val Loss: 80.1800   time: 3.42s   best: 80.1800\n",
      "Epoch 291/1000:  train Loss: 82.6909   val Loss: 80.1655   time: 3.43s   best: 80.1655\n",
      "Epoch 292/1000:  train Loss: 82.5624   val Loss: 80.1188   time: 3.51s   best: 80.1188\n",
      "Epoch 293/1000:  train Loss: 82.6422   val Loss: 80.1585   time: 3.57s   best: 80.1188\n",
      "Epoch 294/1000:  train Loss: 82.6178   val Loss: 80.0491   time: 3.51s   best: 80.0491\n",
      "Epoch 295/1000:  train Loss: 82.5632   val Loss: 80.1813   time: 3.44s   best: 80.0491\n",
      "Epoch 296/1000:  train Loss: 82.4927   val Loss: 79.9393   time: 3.44s   best: 79.9393\n",
      "Epoch 297/1000:  train Loss: 82.4501   val Loss: 79.8881   time: 3.42s   best: 79.8881\n",
      "Epoch 298/1000:  train Loss: 82.4011   val Loss: 79.8880   time: 3.43s   best: 79.8880\n",
      "Epoch 299/1000:  train Loss: 82.4288   val Loss: 79.7771   time: 3.49s   best: 79.7771\n",
      "Epoch 300/1000:  train Loss: 82.2764   val Loss: 79.7981   time: 3.57s   best: 79.7771\n",
      "Epoch 301/1000:  train Loss: 82.3211   val Loss: 79.8133   time: 3.52s   best: 79.7771\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 302/1000:  train Loss: 82.3341   val Loss: 79.7347   time: 3.44s   best: 79.7347\n",
      "Epoch 303/1000:  train Loss: 82.2472   val Loss: 79.8365   time: 3.44s   best: 79.7347\n",
      "Epoch 304/1000:  train Loss: 82.2871   val Loss: 79.6672   time: 3.43s   best: 79.6672\n",
      "Epoch 305/1000:  train Loss: 82.2078   val Loss: 79.6374   time: 3.42s   best: 79.6374\n",
      "Epoch 306/1000:  train Loss: 82.0830   val Loss: 79.5593   time: 3.48s   best: 79.5593\n",
      "Epoch 307/1000:  train Loss: 82.1400   val Loss: 79.5013   time: 3.58s   best: 79.5013\n",
      "Epoch 308/1000:  train Loss: 82.0447   val Loss: 79.5544   time: 3.53s   best: 79.5013\n",
      "Epoch 309/1000:  train Loss: 82.1189   val Loss: 79.5657   time: 3.44s   best: 79.5013\n",
      "Epoch 310/1000:  train Loss: 82.0058   val Loss: 79.4595   time: 3.45s   best: 79.4595\n",
      "Epoch 311/1000:  train Loss: 81.9367   val Loss: 79.3517   time: 3.42s   best: 79.3517\n",
      "Epoch 312/1000:  train Loss: 82.0176   val Loss: 79.4728   time: 3.42s   best: 79.3517\n",
      "Epoch 313/1000:  train Loss: 81.9239   val Loss: 79.3360   time: 3.46s   best: 79.3360\n",
      "Epoch 314/1000:  train Loss: 81.8469   val Loss: 79.3701   time: 3.57s   best: 79.3360\n",
      "Epoch 315/1000:  train Loss: 81.8686   val Loss: 79.2879   time: 3.54s   best: 79.2879\n",
      "Epoch 316/1000:  train Loss: 81.8313   val Loss: 79.5927   time: 3.45s   best: 79.2879\n",
      "Epoch 317/1000:  train Loss: 81.8199   val Loss: 79.3062   time: 3.44s   best: 79.2879\n",
      "Epoch 318/1000:  train Loss: 81.7619   val Loss: 79.2040   time: 3.43s   best: 79.2040\n",
      "Epoch 319/1000:  train Loss: 81.7914   val Loss: 79.0748   time: 3.41s   best: 79.0748\n",
      "Epoch 320/1000:  train Loss: 81.7135   val Loss: 79.1210   time: 3.43s   best: 79.0748\n",
      "Epoch 321/1000:  train Loss: 81.7651   val Loss: 79.0834   time: 3.56s   best: 79.0748\n",
      "Epoch 322/1000:  train Loss: 81.6308   val Loss: 78.9963   time: 3.54s   best: 78.9963\n",
      "Epoch 323/1000:  train Loss: 81.6717   val Loss: 79.0179   time: 3.43s   best: 78.9963\n",
      "Epoch 324/1000:  train Loss: 81.5785   val Loss: 78.9305   time: 3.42s   best: 78.9305\n",
      "Epoch 325/1000:  train Loss: 81.6217   val Loss: 79.0049   time: 3.42s   best: 78.9305\n",
      "Epoch 326/1000:  train Loss: 81.5728   val Loss: 78.9628   time: 3.41s   best: 78.9305\n",
      "Epoch 327/1000:  train Loss: 81.5053   val Loss: 78.9494   time: 3.42s   best: 78.9305\n",
      "Epoch 328/1000:  train Loss: 81.4965   val Loss: 78.9083   time: 3.54s   best: 78.9083\n",
      "Epoch 329/1000:  train Loss: 81.4319   val Loss: 78.8445   time: 3.55s   best: 78.8445\n",
      "Epoch 330/1000:  train Loss: 81.4593   val Loss: 78.8676   time: 3.45s   best: 78.8445\n",
      "Epoch 331/1000:  train Loss: 81.4452   val Loss: 78.8270   time: 3.43s   best: 78.8270\n",
      "Epoch 332/1000:  train Loss: 81.4367   val Loss: 78.8025   time: 3.42s   best: 78.8025\n",
      "Epoch 333/1000:  train Loss: 81.3305   val Loss: 78.7964   time: 3.41s   best: 78.7964\n",
      "Epoch 334/1000:  train Loss: 81.2704   val Loss: 78.7613   time: 3.41s   best: 78.7613\n",
      "Epoch 335/1000:  train Loss: 81.2849   val Loss: 78.7286   time: 3.52s   best: 78.7286\n",
      "Epoch 336/1000:  train Loss: 81.2581   val Loss: 78.7093   time: 3.56s   best: 78.7093\n",
      "Epoch 337/1000:  train Loss: 81.2687   val Loss: 78.7118   time: 3.47s   best: 78.7093\n",
      "Epoch 338/1000:  train Loss: 81.1765   val Loss: 78.7014   time: 3.43s   best: 78.7014\n",
      "Epoch 339/1000:  train Loss: 81.2948   val Loss: 78.6341   time: 3.42s   best: 78.6341\n",
      "Epoch 340/1000:  train Loss: 81.0854   val Loss: 78.6155   time: 3.41s   best: 78.6155\n",
      "Epoch 341/1000:  train Loss: 81.1133   val Loss: 78.6408   time: 3.41s   best: 78.6155\n",
      "Epoch 342/1000:  train Loss: 81.1378   val Loss: 78.4812   time: 3.50s   best: 78.4812\n",
      "Epoch 343/1000:  train Loss: 81.0276   val Loss: 78.4974   time: 3.57s   best: 78.4812\n",
      "Epoch 344/1000:  train Loss: 81.0150   val Loss: 78.5013   time: 3.48s   best: 78.4812\n",
      "Epoch 345/1000:  train Loss: 81.0927   val Loss: 78.4021   time: 3.43s   best: 78.4021\n",
      "Epoch 346/1000:  train Loss: 80.9625   val Loss: 78.3900   time: 3.42s   best: 78.3900\n",
      "Epoch 347/1000:  train Loss: 80.9412   val Loss: 78.3867   time: 3.41s   best: 78.3867\n",
      "Epoch 348/1000:  train Loss: 80.9092   val Loss: 78.3490   time: 3.41s   best: 78.3490\n",
      "Epoch 349/1000:  train Loss: 80.9572   val Loss: 78.3186   time: 3.46s   best: 78.3186\n",
      "Epoch 350/1000:  train Loss: 80.9204   val Loss: 78.2774   time: 3.56s   best: 78.2774\n",
      "Epoch 351/1000:  train Loss: 80.8302   val Loss: 78.2679   time: 3.52s   best: 78.2679\n",
      "Epoch 352/1000:  train Loss: 80.8260   val Loss: 78.2977   time: 3.43s   best: 78.2679\n",
      "Epoch 353/1000:  train Loss: 80.9165   val Loss: 78.3039   time: 3.42s   best: 78.2679\n",
      "Epoch 354/1000:  train Loss: 80.8540   val Loss: 78.2569   time: 3.40s   best: 78.2569\n",
      "Epoch 355/1000:  train Loss: 80.9778   val Loss: 78.2135   time: 3.41s   best: 78.2135\n",
      "Epoch 356/1000:  train Loss: 80.8206   val Loss: 78.2071   time: 3.44s   best: 78.2071\n",
      "Epoch 357/1000:  train Loss: 80.6932   val Loss: 78.2796   time: 3.56s   best: 78.2071\n",
      "Epoch 358/1000:  train Loss: 80.6836   val Loss: 78.1400   time: 3.53s   best: 78.1400\n",
      "Epoch 359/1000:  train Loss: 80.6435   val Loss: 78.2485   time: 3.43s   best: 78.1400\n",
      "Epoch 360/1000:  train Loss: 80.6002   val Loss: 78.1519   time: 3.43s   best: 78.1400\n",
      "Epoch 361/1000:  train Loss: 80.6016   val Loss: 78.0166   time: 3.41s   best: 78.0166\n",
      "Epoch 362/1000:  train Loss: 80.5686   val Loss: 78.1359   time: 3.41s   best: 78.0166\n",
      "Epoch 363/1000:  train Loss: 80.5963   val Loss: 78.0609   time: 3.43s   best: 78.0166\n",
      "Epoch 364/1000:  train Loss: 80.5765   val Loss: 78.1113   time: 3.55s   best: 78.0166\n",
      "Epoch 365/1000:  train Loss: 80.5598   val Loss: 77.9771   time: 3.55s   best: 77.9771\n",
      "Epoch 366/1000:  train Loss: 80.4689   val Loss: 78.0679   time: 3.44s   best: 77.9771\n",
      "Epoch 367/1000:  train Loss: 80.4470   val Loss: 77.9318   time: 3.43s   best: 77.9318\n",
      "Epoch 368/1000:  train Loss: 80.3799   val Loss: 77.9706   time: 3.42s   best: 77.9318\n",
      "Epoch 369/1000:  train Loss: 80.4390   val Loss: 77.9723   time: 3.41s   best: 77.9318\n",
      "Epoch 370/1000:  train Loss: 80.4264   val Loss: 77.8913   time: 3.42s   best: 77.8913\n",
      "Epoch 371/1000:  train Loss: 80.3729   val Loss: 77.9122   time: 3.53s   best: 77.8913\n",
      "Epoch 372/1000:  train Loss: 80.3663   val Loss: 77.8936   time: 3.55s   best: 77.8913\n",
      "Epoch 373/1000:  train Loss: 80.3164   val Loss: 77.9021   time: 3.46s   best: 77.8913\n",
      "Epoch 374/1000:  train Loss: 80.3150   val Loss: 77.8138   time: 3.43s   best: 77.8138\n",
      "Epoch 375/1000:  train Loss: 80.2653   val Loss: 77.8592   time: 3.42s   best: 77.8138\n",
      "Epoch 376/1000:  train Loss: 80.3682   val Loss: 77.8684   time: 3.41s   best: 77.8138\n",
      "Epoch 377/1000:  train Loss: 80.2859   val Loss: 77.8196   time: 3.42s   best: 77.8138\n",
      "Epoch 378/1000:  train Loss: 80.1680   val Loss: 77.7477   time: 3.50s   best: 77.7477\n",
      "Epoch 379/1000:  train Loss: 80.1496   val Loss: 77.7426   time: 3.56s   best: 77.7426\n",
      "Epoch 380/1000:  train Loss: 80.1968   val Loss: 77.7201   time: 3.50s   best: 77.7201\n",
      "Epoch 381/1000:  train Loss: 80.1372   val Loss: 77.6233   time: 3.42s   best: 77.6233\n",
      "Epoch 382/1000:  train Loss: 80.1323   val Loss: 77.7076   time: 3.43s   best: 77.6233\n",
      "Epoch 383/1000:  train Loss: 80.0865   val Loss: 77.6264   time: 3.41s   best: 77.6233\n",
      "Epoch 384/1000:  train Loss: 80.0816   val Loss: 77.6616   time: 3.41s   best: 77.6233\n",
      "Epoch 385/1000:  train Loss: 80.0620   val Loss: 77.5906   time: 3.46s   best: 77.5906\n",
      "Epoch 386/1000:  train Loss: 80.0248   val Loss: 77.6449   time: 3.57s   best: 77.5906\n",
      "Epoch 387/1000:  train Loss: 80.0229   val Loss: 77.6920   time: 3.51s   best: 77.5906\n",
      "Epoch 388/1000:  train Loss: 80.0178   val Loss: 77.6224   time: 3.42s   best: 77.5906\n",
      "Epoch 389/1000:  train Loss: 79.9197   val Loss: 77.4710   time: 3.42s   best: 77.4710\n",
      "Epoch 390/1000:  train Loss: 79.9822   val Loss: 77.5015   time: 3.41s   best: 77.4710\n",
      "Epoch 391/1000:  train Loss: 79.8805   val Loss: 77.4524   time: 3.41s   best: 77.4524\n",
      "Epoch 392/1000:  train Loss: 79.8808   val Loss: 77.4329   time: 3.44s   best: 77.4329\n",
      "Epoch 393/1000:  train Loss: 79.8434   val Loss: 77.4250   time: 3.57s   best: 77.4250\n",
      "Epoch 394/1000:  train Loss: 79.7985   val Loss: 77.4602   time: 3.53s   best: 77.4250\n",
      "Epoch 395/1000:  train Loss: 79.8178   val Loss: 77.4519   time: 3.43s   best: 77.4250\n",
      "Epoch 396/1000:  train Loss: 79.7039   val Loss: 77.5457   time: 3.43s   best: 77.4250\n",
      "Epoch 397/1000:  train Loss: 79.7923   val Loss: 77.4239   time: 3.41s   best: 77.4239\n",
      "Epoch 398/1000:  train Loss: 79.7979   val Loss: 77.3480   time: 3.41s   best: 77.3480\n",
      "Epoch 399/1000:  train Loss: 79.7589   val Loss: 77.3385   time: 3.43s   best: 77.3385\n",
      "Epoch 400/1000:  train Loss: 79.7347   val Loss: 77.4057   time: 3.54s   best: 77.3385\n",
      "Epoch 401/1000:  train Loss: 79.7565   val Loss: 77.3646   time: 3.55s   best: 77.3385\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 402/1000:  train Loss: 79.6516   val Loss: 77.4229   time: 3.45s   best: 77.3385\n",
      "Epoch 403/1000:  train Loss: 79.7074   val Loss: 77.4100   time: 3.43s   best: 77.3385\n",
      "Epoch 404/1000:  train Loss: 79.6236   val Loss: 77.2444   time: 3.42s   best: 77.2444\n",
      "Epoch 405/1000:  train Loss: 79.7014   val Loss: 77.3247   time: 3.41s   best: 77.2444\n",
      "Epoch 406/1000:  train Loss: 79.6049   val Loss: 77.2380   time: 3.41s   best: 77.2380\n",
      "Epoch 407/1000:  train Loss: 79.5772   val Loss: 77.2768   time: 3.49s   best: 77.2380\n",
      "Epoch 408/1000:  train Loss: 79.6523   val Loss: 77.3319   time: 3.57s   best: 77.2380\n",
      "Epoch 409/1000:  train Loss: 79.6521   val Loss: 77.2585   time: 3.49s   best: 77.2380\n",
      "Epoch 410/1000:  train Loss: 79.5041   val Loss: 77.2530   time: 3.43s   best: 77.2380\n",
      "Epoch 411/1000:  train Loss: 79.4711   val Loss: 77.2187   time: 3.43s   best: 77.2187\n",
      "Epoch 412/1000:  train Loss: 79.5256   val Loss: 77.1738   time: 3.41s   best: 77.1738\n",
      "Epoch 413/1000:  train Loss: 79.4646   val Loss: 77.1449   time: 3.41s   best: 77.1449\n",
      "Epoch 414/1000:  train Loss: 79.4978   val Loss: 77.1786   time: 3.45s   best: 77.1449\n",
      "Epoch 415/1000:  train Loss: 79.4642   val Loss: 77.1665   time: 3.57s   best: 77.1449\n",
      "Epoch 416/1000:  train Loss: 79.4313   val Loss: 77.1286   time: 3.52s   best: 77.1286\n",
      "Epoch 417/1000:  train Loss: 79.3922   val Loss: 77.1060   time: 3.43s   best: 77.1060\n",
      "Epoch 418/1000:  train Loss: 79.3436   val Loss: 77.2037   time: 3.43s   best: 77.1060\n",
      "Epoch 419/1000:  train Loss: 79.3599   val Loss: 77.1318   time: 3.41s   best: 77.1060\n",
      "Epoch 420/1000:  train Loss: 79.3936   val Loss: 77.1120   time: 3.41s   best: 77.1060\n",
      "Epoch 421/1000:  train Loss: 79.3929   val Loss: 77.1119   time: 3.44s   best: 77.1060\n",
      "Epoch 422/1000:  train Loss: 79.2616   val Loss: 77.0237   time: 3.57s   best: 77.0237\n",
      "Epoch 423/1000:  train Loss: 79.3725   val Loss: 77.1354   time: 3.56s   best: 77.0237\n",
      "Epoch 424/1000:  train Loss: 79.3317   val Loss: 77.0036   time: 3.46s   best: 77.0036\n",
      "Epoch 425/1000:  train Loss: 79.2873   val Loss: 77.0523   time: 3.44s   best: 77.0036\n",
      "Epoch 426/1000:  train Loss: 79.2148   val Loss: 77.0027   time: 3.43s   best: 77.0027\n",
      "Epoch 427/1000:  train Loss: 79.2647   val Loss: 76.9531   time: 3.43s   best: 76.9531\n",
      "Epoch 428/1000:  train Loss: 79.2436   val Loss: 77.0366   time: 3.43s   best: 76.9531\n",
      "Epoch 429/1000:  train Loss: 79.1636   val Loss: 76.9241   time: 3.53s   best: 76.9241\n",
      "Epoch 430/1000:  train Loss: 79.2235   val Loss: 77.0847   time: 3.58s   best: 76.9241\n",
      "Epoch 431/1000:  train Loss: 79.2029   val Loss: 76.9815   time: 3.49s   best: 76.9241\n",
      "Epoch 432/1000:  train Loss: 79.2117   val Loss: 76.9658   time: 3.45s   best: 76.9241\n",
      "Epoch 433/1000:  train Loss: 79.1812   val Loss: 76.9286   time: 3.44s   best: 76.9241\n",
      "Epoch 434/1000:  train Loss: 79.1807   val Loss: 76.8727   time: 3.43s   best: 76.8727\n",
      "Epoch 435/1000:  train Loss: 79.1133   val Loss: 76.9343   time: 3.42s   best: 76.8727\n",
      "Epoch 436/1000:  train Loss: 79.1958   val Loss: 76.9848   time: 3.50s   best: 76.8727\n",
      "Epoch 437/1000:  train Loss: 79.1020   val Loss: 76.8967   time: 3.58s   best: 76.8727\n",
      "Epoch 438/1000:  train Loss: 79.1168   val Loss: 76.9259   time: 3.51s   best: 76.8727\n",
      "Epoch 439/1000:  train Loss: 79.0111   val Loss: 76.8373   time: 3.44s   best: 76.8373\n",
      "Epoch 440/1000:  train Loss: 79.0373   val Loss: 76.8693   time: 3.44s   best: 76.8373\n",
      "Epoch 441/1000:  train Loss: 78.9970   val Loss: 76.8886   time: 3.42s   best: 76.8373\n",
      "Epoch 442/1000:  train Loss: 79.0397   val Loss: 76.8234   time: 3.43s   best: 76.8234\n",
      "Epoch 443/1000:  train Loss: 79.0213   val Loss: 76.8410   time: 3.49s   best: 76.8234\n",
      "Epoch 444/1000:  train Loss: 78.9185   val Loss: 76.7813   time: 3.59s   best: 76.7813\n",
      "Epoch 445/1000:  train Loss: 78.9567   val Loss: 76.7962   time: 3.52s   best: 76.7813\n",
      "Epoch 446/1000:  train Loss: 78.9890   val Loss: 76.7616   time: 3.45s   best: 76.7616\n",
      "Epoch 447/1000:  train Loss: 79.0498   val Loss: 76.7811   time: 3.43s   best: 76.7616\n",
      "Epoch 448/1000:  train Loss: 78.9640   val Loss: 76.7756   time: 3.40s   best: 76.7616\n",
      "Epoch 449/1000:  train Loss: 78.8928   val Loss: 76.7623   time: 3.41s   best: 76.7616\n",
      "Epoch 450/1000:  train Loss: 78.8320   val Loss: 76.6393   time: 3.45s   best: 76.6393\n",
      "Epoch 451/1000:  train Loss: 78.8406   val Loss: 76.7716   time: 3.57s   best: 76.6393\n",
      "Epoch 452/1000:  train Loss: 78.8660   val Loss: 76.6455   time: 3.52s   best: 76.6393\n",
      "Epoch 453/1000:  train Loss: 78.8470   val Loss: 76.8094   time: 3.43s   best: 76.6393\n",
      "Epoch 454/1000:  train Loss: 78.7840   val Loss: 76.7008   time: 3.43s   best: 76.6393\n",
      "Epoch 455/1000:  train Loss: 78.8527   val Loss: 76.6479   time: 3.41s   best: 76.6393\n",
      "Epoch 456/1000:  train Loss: 78.7551   val Loss: 76.6634   time: 3.41s   best: 76.6393\n",
      "Epoch 457/1000:  train Loss: 78.7547   val Loss: 76.6257   time: 3.44s   best: 76.6257\n",
      "Epoch 458/1000:  train Loss: 78.7636   val Loss: 76.6911   time: 3.57s   best: 76.6257\n",
      "Epoch 459/1000:  train Loss: 78.7723   val Loss: 76.7710   time: 3.53s   best: 76.6257\n",
      "Epoch 460/1000:  train Loss: 78.7495   val Loss: 76.5905   time: 3.43s   best: 76.5905\n",
      "Epoch 461/1000:  train Loss: 78.7001   val Loss: 76.5602   time: 3.43s   best: 76.5602\n",
      "Epoch 462/1000:  train Loss: 78.7414   val Loss: 76.5813   time: 3.41s   best: 76.5602\n",
      "Epoch 463/1000:  train Loss: 78.6419   val Loss: 76.6654   time: 3.41s   best: 76.5602\n",
      "Epoch 464/1000:  train Loss: 78.6402   val Loss: 76.5270   time: 3.42s   best: 76.5270\n",
      "Epoch 465/1000:  train Loss: 78.6742   val Loss: 76.6327   time: 3.54s   best: 76.5270\n",
      "Epoch 466/1000:  train Loss: 78.7051   val Loss: 76.5467   time: 3.55s   best: 76.5270\n",
      "Epoch 467/1000:  train Loss: 78.6242   val Loss: 76.5172   time: 3.45s   best: 76.5172\n",
      "Epoch 468/1000:  train Loss: 78.6562   val Loss: 76.5952   time: 3.43s   best: 76.5172\n",
      "Epoch 469/1000:  train Loss: 78.6118   val Loss: 76.5086   time: 3.41s   best: 76.5086\n",
      "Epoch 470/1000:  train Loss: 78.5450   val Loss: 76.3481   time: 3.41s   best: 76.3481\n",
      "Epoch 471/1000:  train Loss: 78.4958   val Loss: 76.4657   time: 3.41s   best: 76.3481\n",
      "Epoch 472/1000:  train Loss: 78.6380   val Loss: 76.5101   time: 3.51s   best: 76.3481\n",
      "Epoch 473/1000:  train Loss: 78.5016   val Loss: 76.3885   time: 3.56s   best: 76.3481\n",
      "Epoch 474/1000:  train Loss: 78.5357   val Loss: 76.4626   time: 3.47s   best: 76.3481\n",
      "Epoch 475/1000:  train Loss: 78.4857   val Loss: 76.4588   time: 3.42s   best: 76.3481\n",
      "Epoch 476/1000:  train Loss: 78.5559   val Loss: 76.3754   time: 3.42s   best: 76.3481\n",
      "Epoch 477/1000:  train Loss: 78.4615   val Loss: 76.3796   time: 3.40s   best: 76.3481\n",
      "Epoch 478/1000:  train Loss: 78.4231   val Loss: 76.3562   time: 3.41s   best: 76.3481\n",
      "Epoch 479/1000:  train Loss: 78.4040   val Loss: 76.3333   time: 3.48s   best: 76.3333\n",
      "Epoch 480/1000:  train Loss: 78.4438   val Loss: 76.2631   time: 3.57s   best: 76.2631\n",
      "Epoch 481/1000:  train Loss: 78.4205   val Loss: 76.4543   time: 3.49s   best: 76.2631\n",
      "Epoch 482/1000:  train Loss: 78.4808   val Loss: 76.3183   time: 3.42s   best: 76.2631\n",
      "Epoch 483/1000:  train Loss: 78.5142   val Loss: 76.3279   time: 3.42s   best: 76.2631\n",
      "Epoch 484/1000:  train Loss: 78.3656   val Loss: 76.2182   time: 3.41s   best: 76.2182\n",
      "Epoch 485/1000:  train Loss: 78.3782   val Loss: 76.4486   time: 3.41s   best: 76.2182\n",
      "Epoch 486/1000:  train Loss: 78.4491   val Loss: 76.3447   time: 3.46s   best: 76.2182\n",
      "Epoch 487/1000:  train Loss: 78.3635   val Loss: 76.2705   time: 3.57s   best: 76.2182\n",
      "Epoch 488/1000:  train Loss: 78.4088   val Loss: 76.2112   time: 3.51s   best: 76.2112\n",
      "Epoch 489/1000:  train Loss: 78.2935   val Loss: 76.2526   time: 3.42s   best: 76.2112\n",
      "Epoch 490/1000:  train Loss: 78.3530   val Loss: 76.3446   time: 3.42s   best: 76.2112\n",
      "Epoch 491/1000:  train Loss: 78.2970   val Loss: 76.2556   time: 3.41s   best: 76.2112\n",
      "Epoch 492/1000:  train Loss: 78.3394   val Loss: 76.1704   time: 3.41s   best: 76.1704\n",
      "Epoch 493/1000:  train Loss: 78.3109   val Loss: 76.3055   time: 3.44s   best: 76.1704\n",
      "Epoch 494/1000:  train Loss: 78.2590   val Loss: 76.1464   time: 3.57s   best: 76.1464\n",
      "Epoch 495/1000:  train Loss: 78.2222   val Loss: 76.1803   time: 3.52s   best: 76.1464\n",
      "Epoch 496/1000:  train Loss: 78.1958   val Loss: 76.1841   time: 3.42s   best: 76.1464\n",
      "Epoch 497/1000:  train Loss: 78.2450   val Loss: 76.1610   time: 3.43s   best: 76.1464\n",
      "Epoch 498/1000:  train Loss: 78.1443   val Loss: 76.2402   time: 3.41s   best: 76.1464\n",
      "Epoch 499/1000:  train Loss: 78.1628   val Loss: 76.1412   time: 3.41s   best: 76.1412\n",
      "Epoch 500/1000:  train Loss: 78.1819   val Loss: 76.1644   time: 3.43s   best: 76.1412\n",
      "Epoch 501/1000:  train Loss: 78.3081   val Loss: 76.1719   time: 3.55s   best: 76.1412\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 502/1000:  train Loss: 78.2029   val Loss: 76.1071   time: 3.55s   best: 76.1071\n",
      "Epoch 503/1000:  train Loss: 78.0922   val Loss: 76.1241   time: 3.44s   best: 76.1071\n",
      "Epoch 504/1000:  train Loss: 78.0608   val Loss: 76.2180   time: 3.43s   best: 76.1071\n",
      "Epoch 505/1000:  train Loss: 78.1274   val Loss: 75.9681   time: 3.42s   best: 75.9681\n",
      "Epoch 506/1000:  train Loss: 78.0662   val Loss: 75.9966   time: 3.41s   best: 75.9681\n",
      "Epoch 507/1000:  train Loss: 78.0138   val Loss: 76.0185   time: 3.42s   best: 75.9681\n",
      "Epoch 508/1000:  train Loss: 78.0871   val Loss: 76.0094   time: 3.52s   best: 75.9681\n",
      "Epoch 509/1000:  train Loss: 78.0431   val Loss: 75.8700   time: 3.56s   best: 75.8700\n",
      "Epoch 510/1000:  train Loss: 78.0066   val Loss: 76.0254   time: 3.47s   best: 75.8700\n",
      "Epoch 511/1000:  train Loss: 78.0050   val Loss: 75.9901   time: 3.42s   best: 75.8700\n",
      "Epoch 512/1000:  train Loss: 78.0538   val Loss: 76.0086   time: 3.42s   best: 75.8700\n",
      "Epoch 513/1000:  train Loss: 78.0381   val Loss: 76.0031   time: 3.41s   best: 75.8700\n",
      "Epoch 514/1000:  train Loss: 78.0222   val Loss: 75.9192   time: 3.41s   best: 75.8700\n",
      "Epoch 515/1000:  train Loss: 78.0211   val Loss: 76.0279   time: 3.49s   best: 75.8700\n",
      "Epoch 516/1000:  train Loss: 77.9099   val Loss: 75.9116   time: 3.56s   best: 75.8700\n",
      "Epoch 517/1000:  train Loss: 77.9431   val Loss: 75.9628   time: 3.49s   best: 75.8700\n",
      "Epoch 518/1000:  train Loss: 77.8838   val Loss: 75.8647   time: 3.42s   best: 75.8647\n",
      "Epoch 519/1000:  train Loss: 77.9930   val Loss: 76.0336   time: 3.43s   best: 75.8647\n",
      "Epoch 520/1000:  train Loss: 77.9432   val Loss: 75.8410   time: 3.41s   best: 75.8410\n",
      "Epoch 521/1000:  train Loss: 77.8677   val Loss: 75.9031   time: 3.41s   best: 75.8410\n",
      "Epoch 522/1000:  train Loss: 77.9231   val Loss: 75.7309   time: 3.47s   best: 75.7309\n",
      "Epoch 523/1000:  train Loss: 77.8646   val Loss: 75.8021   time: 3.58s   best: 75.7309\n",
      "Epoch 524/1000:  train Loss: 77.9232   val Loss: 75.7601   time: 3.52s   best: 75.7309\n",
      "Epoch 525/1000:  train Loss: 77.8211   val Loss: 75.8884   time: 3.43s   best: 75.7309\n",
      "Epoch 526/1000:  train Loss: 77.8050   val Loss: 75.8103   time: 3.44s   best: 75.7309\n",
      "Epoch 527/1000:  train Loss: 77.7642   val Loss: 75.8717   time: 3.42s   best: 75.7309\n",
      "Epoch 528/1000:  train Loss: 77.8267   val Loss: 75.8990   time: 3.42s   best: 75.7309\n",
      "Epoch 529/1000:  train Loss: 77.8334   val Loss: 75.7413   time: 3.46s   best: 75.7309\n",
      "Epoch 530/1000:  train Loss: 77.7580   val Loss: 75.7356   time: 3.58s   best: 75.7309\n",
      "Epoch 531/1000:  train Loss: 77.7363   val Loss: 75.7150   time: 3.53s   best: 75.7150\n",
      "Epoch 532/1000:  train Loss: 77.8017   val Loss: 75.6232   time: 3.44s   best: 75.6232\n",
      "Epoch 533/1000:  train Loss: 77.7269   val Loss: 75.7267   time: 3.44s   best: 75.6232\n",
      "Epoch 534/1000:  train Loss: 77.7482   val Loss: 75.6942   time: 3.42s   best: 75.6232\n",
      "Epoch 535/1000:  train Loss: 77.7387   val Loss: 75.7355   time: 3.42s   best: 75.6232\n",
      "Epoch 536/1000:  train Loss: 77.8738   val Loss: 75.7049   time: 3.45s   best: 75.6232\n",
      "Epoch 537/1000:  train Loss: 77.6733   val Loss: 75.7406   time: 3.57s   best: 75.6232\n",
      "Epoch 538/1000:  train Loss: 77.6918   val Loss: 75.6606   time: 3.54s   best: 75.6232\n",
      "Epoch 539/1000:  train Loss: 77.6675   val Loss: 75.7234   time: 3.44s   best: 75.6232\n",
      "Epoch 540/1000:  train Loss: 77.6595   val Loss: 75.7244   time: 3.44s   best: 75.6232\n",
      "Epoch 541/1000:  train Loss: 77.7151   val Loss: 75.5975   time: 3.42s   best: 75.5975\n",
      "Epoch 542/1000:  train Loss: 77.6494   val Loss: 75.7054   time: 3.42s   best: 75.5975\n",
      "Epoch 543/1000:  train Loss: 77.6413   val Loss: 75.5597   time: 3.42s   best: 75.5597\n",
      "Epoch 544/1000:  train Loss: 77.5554   val Loss: 75.5567   time: 3.55s   best: 75.5567\n",
      "Epoch 545/1000:  train Loss: 77.6010   val Loss: 75.6550   time: 3.55s   best: 75.5567\n",
      "Epoch 546/1000:  train Loss: 77.5732   val Loss: 75.5985   time: 3.45s   best: 75.5567\n",
      "Epoch 547/1000:  train Loss: 77.5231   val Loss: 75.6201   time: 3.43s   best: 75.5567\n",
      "Epoch 548/1000:  train Loss: 77.5481   val Loss: 75.6876   time: 3.42s   best: 75.5567\n",
      "Epoch 549/1000:  train Loss: 77.5531   val Loss: 75.5501   time: 3.41s   best: 75.5501\n",
      "Epoch 550/1000:  train Loss: 77.5039   val Loss: 75.5808   time: 3.42s   best: 75.5501\n",
      "Epoch 551/1000:  train Loss: 77.4345   val Loss: 75.4142   time: 3.51s   best: 75.4142\n",
      "Epoch 552/1000:  train Loss: 77.5236   val Loss: 75.4567   time: 3.57s   best: 75.4142\n",
      "Epoch 553/1000:  train Loss: 77.5318   val Loss: 75.6081   time: 3.48s   best: 75.4142\n",
      "Epoch 554/1000:  train Loss: 77.5922   val Loss: 75.5554   time: 3.43s   best: 75.4142\n",
      "Epoch 555/1000:  train Loss: 77.4161   val Loss: 75.3527   time: 3.43s   best: 75.3527\n",
      "Epoch 556/1000:  train Loss: 77.4670   val Loss: 75.5588   time: 3.41s   best: 75.3527\n",
      "Epoch 557/1000:  train Loss: 77.3760   val Loss: 75.4507   time: 3.42s   best: 75.3527\n",
      "Epoch 558/1000:  train Loss: 77.4735   val Loss: 75.5466   time: 3.49s   best: 75.3527\n",
      "Epoch 559/1000:  train Loss: 77.4000   val Loss: 75.4131   time: 3.57s   best: 75.3527\n",
      "Epoch 560/1000:  train Loss: 77.3887   val Loss: 75.4461   time: 3.50s   best: 75.3527\n",
      "Epoch 561/1000:  train Loss: 77.3538   val Loss: 75.4542   time: 3.43s   best: 75.3527\n",
      "Epoch 562/1000:  train Loss: 77.3652   val Loss: 75.4176   time: 3.43s   best: 75.3527\n",
      "Epoch 563/1000:  train Loss: 77.3772   val Loss: 75.4844   time: 3.41s   best: 75.3527\n",
      "Epoch 564/1000:  train Loss: 77.3717   val Loss: 75.4525   time: 3.41s   best: 75.3527\n",
      "Epoch 565/1000:  train Loss: 77.2940   val Loss: 75.4802   time: 3.44s   best: 75.3527\n",
      "Epoch 566/1000:  train Loss: 77.3447   val Loss: 75.3343   time: 3.57s   best: 75.3343\n",
      "Epoch 567/1000:  train Loss: 77.4032   val Loss: 75.2290   time: 3.53s   best: 75.2290\n",
      "Epoch 568/1000:  train Loss: 77.3226   val Loss: 75.4595   time: 3.44s   best: 75.2290\n",
      "Epoch 569/1000:  train Loss: 77.3225   val Loss: 75.5512   time: 3.43s   best: 75.2290\n",
      "Epoch 570/1000:  train Loss: 77.2894   val Loss: 75.5596   time: 3.42s   best: 75.2290\n",
      "Epoch 571/1000:  train Loss: 77.3127   val Loss: 75.3206   time: 3.42s   best: 75.2290\n",
      "Epoch 572/1000:  train Loss: 77.3373   val Loss: 75.4412   time: 3.43s   best: 75.2290\n",
      "Epoch 573/1000:  train Loss: 77.3166   val Loss: 75.3012   time: 3.55s   best: 75.2290\n",
      "Epoch 574/1000:  train Loss: 77.2669   val Loss: 75.3632   time: 3.55s   best: 75.2290\n",
      "Epoch 575/1000:  train Loss: 77.2916   val Loss: 75.3356   time: 3.45s   best: 75.2290\n",
      "Epoch 576/1000:  train Loss: 77.3333   val Loss: 75.2775   time: 3.43s   best: 75.2290\n",
      "Epoch 577/1000:  train Loss: 77.2088   val Loss: 75.2138   time: 3.42s   best: 75.2138\n",
      "Epoch 578/1000:  train Loss: 77.2625   val Loss: 75.2964   time: 3.41s   best: 75.2138\n",
      "Epoch 579/1000:  train Loss: 77.1488   val Loss: 75.5047   time: 3.41s   best: 75.2138\n",
      "Epoch 580/1000:  train Loss: 77.2703   val Loss: 75.1924   time: 3.52s   best: 75.1924\n",
      "Epoch 581/1000:  train Loss: 77.2172   val Loss: 75.3770   time: 3.56s   best: 75.1924\n",
      "Epoch 582/1000:  train Loss: 77.1797   val Loss: 75.2183   time: 3.47s   best: 75.1924\n",
      "Epoch 583/1000:  train Loss: 77.1569   val Loss: 75.2830   time: 3.43s   best: 75.1924\n",
      "Epoch 584/1000:  train Loss: 77.1137   val Loss: 75.1970   time: 3.43s   best: 75.1924\n",
      "Epoch 585/1000:  train Loss: 77.0630   val Loss: 75.4649   time: 3.41s   best: 75.1924\n",
      "Epoch 586/1000:  train Loss: 77.0993   val Loss: 75.2641   time: 3.41s   best: 75.1924\n",
      "Epoch 587/1000:  train Loss: 77.0882   val Loss: 75.2274   time: 3.48s   best: 75.1924\n",
      "Epoch 588/1000:  train Loss: 77.0315   val Loss: 75.1517   time: 3.57s   best: 75.1517\n",
      "Epoch 589/1000:  train Loss: 77.0867   val Loss: 75.2764   time: 3.49s   best: 75.1517\n",
      "Epoch 590/1000:  train Loss: 77.0304   val Loss: 75.2716   time: 3.42s   best: 75.1517\n",
      "Epoch 591/1000:  train Loss: 77.0447   val Loss: 75.1497   time: 3.42s   best: 75.1497\n",
      "Epoch 592/1000:  train Loss: 77.1115   val Loss: 75.2770   time: 3.41s   best: 75.1497\n",
      "Epoch 593/1000:  train Loss: 77.0378   val Loss: 75.1570   time: 3.41s   best: 75.1497\n",
      "Epoch 594/1000:  train Loss: 77.0719   val Loss: 75.2571   time: 3.45s   best: 75.1497\n",
      "Epoch 595/1000:  train Loss: 77.0839   val Loss: 75.0880   time: 3.57s   best: 75.0880\n",
      "Epoch 596/1000:  train Loss: 76.9670   val Loss: 75.1002   time: 3.52s   best: 75.0880\n",
      "Epoch 597/1000:  train Loss: 77.0264   val Loss: 75.4684   time: 3.43s   best: 75.0880\n",
      "Epoch 598/1000:  train Loss: 76.9813   val Loss: 75.1001   time: 3.43s   best: 75.0880\n",
      "Epoch 599/1000:  train Loss: 76.9363   val Loss: 75.0511   time: 3.41s   best: 75.0511\n",
      "Epoch 600/1000:  train Loss: 76.9651   val Loss: 75.0906   time: 3.41s   best: 75.0511\n",
      "Epoch 601/1000:  train Loss: 76.8945   val Loss: 75.0311   time: 3.43s   best: 75.0311\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 602/1000:  train Loss: 76.9487   val Loss: 74.9840   time: 3.56s   best: 74.9840\n",
      "Epoch 603/1000:  train Loss: 76.9346   val Loss: 75.1019   time: 3.55s   best: 74.9840\n",
      "Epoch 604/1000:  train Loss: 76.9620   val Loss: 75.2385   time: 3.44s   best: 74.9840\n",
      "Epoch 605/1000:  train Loss: 76.9037   val Loss: 75.1818   time: 3.43s   best: 74.9840\n",
      "Epoch 606/1000:  train Loss: 76.9020   val Loss: 75.0360   time: 3.42s   best: 74.9840\n",
      "Epoch 607/1000:  train Loss: 76.9379   val Loss: 75.0767   time: 3.41s   best: 74.9840\n",
      "Epoch 608/1000:  train Loss: 76.8532   val Loss: 75.0090   time: 3.42s   best: 74.9840\n",
      "Epoch 609/1000:  train Loss: 76.8146   val Loss: 74.9804   time: 3.53s   best: 74.9804\n",
      "Epoch 610/1000:  train Loss: 76.8555   val Loss: 74.9849   time: 3.56s   best: 74.9804\n",
      "Epoch 611/1000:  train Loss: 76.7662   val Loss: 75.2360   time: 3.47s   best: 74.9804\n",
      "Epoch 612/1000:  train Loss: 76.7560   val Loss: 75.1143   time: 3.43s   best: 74.9804\n",
      "Epoch 613/1000:  train Loss: 76.8003   val Loss: 75.0292   time: 3.43s   best: 74.9804\n",
      "Epoch 614/1000:  train Loss: 76.8192   val Loss: 75.0809   time: 3.41s   best: 74.9804\n",
      "Epoch 615/1000:  train Loss: 76.7340   val Loss: 75.1382   time: 3.41s   best: 74.9804\n",
      "Epoch 616/1000:  train Loss: 76.7729   val Loss: 75.1739   time: 3.49s   best: 74.9804\n",
      "Epoch 617/1000:  train Loss: 76.7809   val Loss: 74.9061   time: 3.56s   best: 74.9061\n",
      "Epoch 618/1000:  train Loss: 76.7978   val Loss: 75.0113   time: 3.50s   best: 74.9061\n",
      "Epoch 619/1000:  train Loss: 76.8110   val Loss: 74.9210   time: 3.43s   best: 74.9061\n",
      "Epoch 620/1000:  train Loss: 76.7391   val Loss: 75.0847   time: 3.43s   best: 74.9061\n",
      "Epoch 621/1000:  train Loss: 76.6985   val Loss: 74.8707   time: 3.42s   best: 74.8707\n",
      "Epoch 622/1000:  train Loss: 76.7187   val Loss: 74.8237   time: 3.41s   best: 74.8237\n",
      "Epoch 623/1000:  train Loss: 76.8169   val Loss: 75.0298   time: 3.46s   best: 74.8237\n",
      "Epoch 624/1000:  train Loss: 76.7522   val Loss: 74.8793   time: 3.58s   best: 74.8237\n",
      "Epoch 625/1000:  train Loss: 76.6901   val Loss: 74.9086   time: 3.52s   best: 74.8237\n",
      "Epoch 626/1000:  train Loss: 76.7587   val Loss: 74.8451   time: 3.44s   best: 74.8237\n",
      "Epoch 627/1000:  train Loss: 76.6492   val Loss: 74.9614   time: 3.44s   best: 74.8237\n",
      "Epoch 628/1000:  train Loss: 76.7144   val Loss: 74.7926   time: 3.41s   best: 74.7926\n",
      "Epoch 629/1000:  train Loss: 76.6611   val Loss: 74.9215   time: 3.41s   best: 74.7926\n",
      "Epoch 630/1000:  train Loss: 76.5772   val Loss: 74.9245   time: 3.44s   best: 74.7926\n",
      "Epoch 631/1000:  train Loss: 76.5497   val Loss: 74.8349   time: 3.57s   best: 74.7926\n",
      "Epoch 632/1000:  train Loss: 76.6517   val Loss: 74.9322   time: 3.53s   best: 74.7926\n",
      "Epoch 633/1000:  train Loss: 76.5602   val Loss: 75.1577   time: 3.43s   best: 74.7926\n",
      "Epoch 634/1000:  train Loss: 76.6028   val Loss: 74.7695   time: 3.43s   best: 74.7695\n",
      "Epoch 635/1000:  train Loss: 76.5509   val Loss: 74.8742   time: 3.42s   best: 74.7695\n",
      "Epoch 636/1000:  train Loss: 76.4925   val Loss: 74.7141   time: 3.42s   best: 74.7141\n",
      "Epoch 637/1000:  train Loss: 76.6038   val Loss: 74.7345   time: 3.42s   best: 74.7141\n",
      "Epoch 638/1000:  train Loss: 76.6406   val Loss: 74.7286   time: 3.54s   best: 74.7141\n",
      "Epoch 639/1000:  train Loss: 76.4993   val Loss: 74.8963   time: 3.54s   best: 74.7141\n",
      "Epoch 640/1000:  train Loss: 76.5184   val Loss: 74.7507   time: 3.44s   best: 74.7141\n",
      "Epoch 641/1000:  train Loss: 76.5057   val Loss: 74.9540   time: 3.43s   best: 74.7141\n",
      "Epoch 642/1000:  train Loss: 76.4868   val Loss: 74.8065   time: 3.42s   best: 74.7141\n",
      "Epoch 643/1000:  train Loss: 76.4644   val Loss: 74.6624   time: 3.40s   best: 74.6624\n",
      "Epoch 644/1000:  train Loss: 76.5447   val Loss: 74.6306   time: 3.42s   best: 74.6306\n",
      "Epoch 645/1000:  train Loss: 76.4518   val Loss: 74.9666   time: 3.50s   best: 74.6306\n",
      "Epoch 646/1000:  train Loss: 76.4889   val Loss: 74.6439   time: 3.56s   best: 74.6306\n",
      "Epoch 647/1000:  train Loss: 76.4548   val Loss: 74.6021   time: 3.48s   best: 74.6021\n",
      "Epoch 648/1000:  train Loss: 76.4507   val Loss: 74.8179   time: 3.43s   best: 74.6021\n",
      "Epoch 649/1000:  train Loss: 76.4492   val Loss: 74.8563   time: 3.43s   best: 74.6021\n",
      "Epoch 650/1000:  train Loss: 76.5469   val Loss: 74.6407   time: 3.41s   best: 74.6021\n",
      "Epoch 651/1000:  train Loss: 76.3649   val Loss: 74.5732   time: 3.41s   best: 74.5732\n",
      "Epoch 652/1000:  train Loss: 76.3048   val Loss: 74.8323   time: 3.48s   best: 74.5732\n",
      "Epoch 653/1000:  train Loss: 76.3771   val Loss: 74.5625   time: 3.57s   best: 74.5625\n",
      "Epoch 654/1000:  train Loss: 76.3475   val Loss: 74.5289   time: 3.50s   best: 74.5289\n",
      "Epoch 655/1000:  train Loss: 76.4098   val Loss: 74.6107   time: 3.43s   best: 74.5289\n",
      "Epoch 656/1000:  train Loss: 76.3723   val Loss: 74.6402   time: 3.43s   best: 74.5289\n",
      "Epoch 657/1000:  train Loss: 76.3504   val Loss: 74.5896   time: 3.41s   best: 74.5289\n",
      "Epoch 658/1000:  train Loss: 76.4265   val Loss: 74.7852   time: 3.41s   best: 74.5289\n",
      "Epoch 659/1000:  train Loss: 76.3873   val Loss: 75.2983   time: 3.44s   best: 74.5289\n",
      "Epoch 660/1000:  train Loss: 76.5428   val Loss: 74.5483   time: 3.57s   best: 74.5289\n",
      "Epoch 661/1000:  train Loss: 76.3093   val Loss: 74.5382   time: 3.53s   best: 74.5289\n",
      "Epoch 662/1000:  train Loss: 76.3019   val Loss: 74.7393   time: 3.43s   best: 74.5289\n",
      "Epoch 663/1000:  train Loss: 76.3031   val Loss: 74.4909   time: 3.43s   best: 74.4909\n",
      "Epoch 664/1000:  train Loss: 76.4126   val Loss: 74.5158   time: 3.42s   best: 74.4909\n",
      "Epoch 665/1000:  train Loss: 76.4220   val Loss: 74.7516   time: 3.42s   best: 74.4909\n",
      "Epoch 666/1000:  train Loss: 76.2771   val Loss: 74.6843   time: 3.44s   best: 74.4909\n",
      "Epoch 667/1000:  train Loss: 76.2293   val Loss: 74.4935   time: 3.56s   best: 74.4909\n",
      "Epoch 668/1000:  train Loss: 76.1994   val Loss: 74.5910   time: 3.54s   best: 74.4909\n",
      "Epoch 669/1000:  train Loss: 76.2389   val Loss: 74.4829   time: 3.44s   best: 74.4829\n",
      "Epoch 670/1000:  train Loss: 76.2058   val Loss: 74.7327   time: 3.43s   best: 74.4829\n",
      "Epoch 671/1000:  train Loss: 76.2145   val Loss: 74.5444   time: 3.42s   best: 74.4829\n",
      "Epoch 672/1000:  train Loss: 76.2494   val Loss: 74.3560   time: 3.41s   best: 74.3560\n",
      "Epoch 673/1000:  train Loss: 76.1479   val Loss: 74.4232   time: 3.42s   best: 74.3560\n",
      "Epoch 674/1000:  train Loss: 76.2743   val Loss: 74.5581   time: 3.51s   best: 74.3560\n",
      "Epoch 675/1000:  train Loss: 76.2922   val Loss: 74.3685   time: 3.56s   best: 74.3560\n",
      "Epoch 676/1000:  train Loss: 76.1631   val Loss: 74.3342   time: 3.48s   best: 74.3342\n",
      "Epoch 677/1000:  train Loss: 76.2924   val Loss: 74.2719   time: 3.44s   best: 74.2719\n",
      "Epoch 678/1000:  train Loss: 76.2350   val Loss: 74.5109   time: 3.42s   best: 74.2719\n",
      "Epoch 679/1000:  train Loss: 76.2619   val Loss: 74.3959   time: 3.41s   best: 74.2719\n",
      "Epoch 680/1000:  train Loss: 76.1077   val Loss: 74.5160   time: 3.41s   best: 74.2719\n",
      "Epoch 681/1000:  train Loss: 76.0976   val Loss: 74.3498   time: 3.50s   best: 74.2719\n",
      "Epoch 682/1000:  train Loss: 76.1610   val Loss: 74.3582   time: 3.57s   best: 74.2719\n",
      "Epoch 683/1000:  train Loss: 76.1422   val Loss: 74.5599   time: 3.49s   best: 74.2719\n",
      "Epoch 684/1000:  train Loss: 76.0538   val Loss: 74.3492   time: 3.43s   best: 74.2719\n",
      "Epoch 685/1000:  train Loss: 76.1369   val Loss: 74.3670   time: 3.43s   best: 74.2719\n",
      "Epoch 686/1000:  train Loss: 76.0903   val Loss: 74.5696   time: 3.41s   best: 74.2719\n",
      "Epoch 687/1000:  train Loss: 76.0899   val Loss: 74.3078   time: 3.41s   best: 74.2719\n",
      "Epoch 688/1000:  train Loss: 76.2462   val Loss: 74.3770   time: 3.47s   best: 74.2719\n",
      "Epoch 689/1000:  train Loss: 76.0964   val Loss: 74.2237   time: 3.58s   best: 74.2237\n",
      "Epoch 690/1000:  train Loss: 76.0818   val Loss: 74.3954   time: 3.50s   best: 74.2237\n",
      "Epoch 691/1000:  train Loss: 76.0978   val Loss: 74.1778   time: 3.42s   best: 74.1778\n",
      "Epoch 692/1000:  train Loss: 76.0409   val Loss: 74.2109   time: 3.44s   best: 74.1778\n",
      "Epoch 693/1000:  train Loss: 76.0490   val Loss: 74.3882   time: 3.41s   best: 74.1778\n",
      "Epoch 694/1000:  train Loss: 76.0063   val Loss: 74.3026   time: 3.41s   best: 74.1778\n",
      "Epoch 695/1000:  train Loss: 76.0396   val Loss: 74.2431   time: 3.45s   best: 74.1778\n",
      "Epoch 696/1000:  train Loss: 76.0463   val Loss: 74.5537   time: 3.57s   best: 74.1778\n",
      "Epoch 697/1000:  train Loss: 76.0528   val Loss: 74.3877   time: 3.53s   best: 74.1778\n",
      "Epoch 698/1000:  train Loss: 76.0171   val Loss: 74.2902   time: 3.44s   best: 74.1778\n",
      "Epoch 699/1000:  train Loss: 76.0064   val Loss: 74.3972   time: 3.43s   best: 74.1778\n",
      "Epoch 700/1000:  train Loss: 75.9327   val Loss: 74.3094   time: 3.42s   best: 74.1778\n",
      "Epoch 701/1000:  train Loss: 75.9813   val Loss: 74.7725   time: 3.41s   best: 74.1778\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 702/1000:  train Loss: 75.9857   val Loss: 74.2472   time: 3.43s   best: 74.1778\n",
      "Epoch 703/1000:  train Loss: 75.9661   val Loss: 74.2138   time: 3.54s   best: 74.1778\n",
      "Epoch 704/1000:  train Loss: 75.9327   val Loss: 74.2056   time: 3.56s   best: 74.1778\n",
      "Epoch 705/1000:  train Loss: 76.0043   val Loss: 74.3896   time: 3.45s   best: 74.1778\n",
      "Epoch 706/1000:  train Loss: 75.9917   val Loss: 74.5312   time: 3.44s   best: 74.1778\n",
      "Epoch 707/1000:  train Loss: 76.0256   val Loss: 74.2073   time: 3.43s   best: 74.1778\n",
      "Epoch 708/1000:  train Loss: 75.9162   val Loss: 74.1335   time: 3.41s   best: 74.1335\n",
      "Epoch 709/1000:  train Loss: 75.8650   val Loss: 74.1151   time: 3.41s   best: 74.1151\n",
      "Epoch 710/1000:  train Loss: 75.9144   val Loss: 74.3700   time: 3.50s   best: 74.1151\n",
      "Epoch 711/1000:  train Loss: 75.9039   val Loss: 74.3080   time: 3.57s   best: 74.1151\n",
      "Epoch 712/1000:  train Loss: 75.8576   val Loss: 74.2199   time: 3.49s   best: 74.1151\n",
      "Epoch 713/1000:  train Loss: 75.8858   val Loss: 74.1635   time: 3.43s   best: 74.1151\n",
      "Epoch 714/1000:  train Loss: 76.0360   val Loss: 74.0557   time: 3.43s   best: 74.0557\n",
      "Epoch 715/1000:  train Loss: 75.7349   val Loss: 74.1197   time: 3.41s   best: 74.0557\n",
      "Epoch 716/1000:  train Loss: 75.7965   val Loss: 74.0705   time: 3.41s   best: 74.0557\n",
      "Epoch 717/1000:  train Loss: 75.8726   val Loss: 74.1013   time: 3.47s   best: 74.0557\n",
      "Epoch 718/1000:  train Loss: 75.7862   val Loss: 74.1667   time: 3.57s   best: 74.0557\n",
      "Epoch 719/1000:  train Loss: 75.8678   val Loss: 74.2781   time: 3.50s   best: 74.0557\n",
      "Epoch 720/1000:  train Loss: 75.8322   val Loss: 74.0423   time: 3.43s   best: 74.0423\n",
      "Epoch 721/1000:  train Loss: 75.8219   val Loss: 74.0964   time: 3.43s   best: 74.0423\n",
      "Epoch 722/1000:  train Loss: 75.7700   val Loss: 75.1931   time: 3.41s   best: 74.0423\n",
      "Epoch 723/1000:  train Loss: 75.7546   val Loss: 74.4653   time: 3.42s   best: 74.0423\n",
      "Epoch 724/1000:  train Loss: 75.7546   val Loss: 74.0987   time: 3.45s   best: 74.0423\n",
      "Epoch 725/1000:  train Loss: 75.8411   val Loss: 74.3332   time: 3.58s   best: 74.0423\n",
      "Epoch 726/1000:  train Loss: 75.8514   val Loss: 74.0579   time: 3.53s   best: 74.0423\n",
      "Epoch 727/1000:  train Loss: 75.7856   val Loss: 73.9897   time: 3.44s   best: 73.9897\n",
      "Epoch 728/1000:  train Loss: 75.7479   val Loss: 74.0873   time: 3.43s   best: 73.9897\n",
      "Epoch 729/1000:  train Loss: 75.7317   val Loss: 74.3452   time: 3.41s   best: 73.9897\n",
      "Epoch 730/1000:  train Loss: 75.7239   val Loss: 73.9910   time: 3.41s   best: 73.9897\n",
      "Epoch 731/1000:  train Loss: 75.6841   val Loss: 74.0544   time: 3.43s   best: 73.9897\n",
      "Epoch 732/1000:  train Loss: 75.6980   val Loss: 74.1926   time: 3.55s   best: 73.9897\n",
      "Epoch 733/1000:  train Loss: 75.6952   val Loss: 73.9733   time: 3.54s   best: 73.9733\n",
      "Epoch 734/1000:  train Loss: 75.6894   val Loss: 74.0323   time: 3.45s   best: 73.9733\n",
      "Epoch 735/1000:  train Loss: 75.6796   val Loss: 73.9804   time: 3.43s   best: 73.9733\n",
      "Epoch 736/1000:  train Loss: 75.6963   val Loss: 74.0346   time: 3.42s   best: 73.9733\n",
      "Epoch 737/1000:  train Loss: 75.6242   val Loss: 73.9941   time: 3.41s   best: 73.9733\n",
      "Epoch 738/1000:  train Loss: 75.6657   val Loss: 74.2034   time: 3.42s   best: 73.9733\n",
      "Epoch 739/1000:  train Loss: 75.6190   val Loss: 73.9512   time: 3.53s   best: 73.9512\n",
      "Epoch 740/1000:  train Loss: 75.6481   val Loss: 73.9948   time: 3.56s   best: 73.9512\n",
      "Epoch 741/1000:  train Loss: 75.6875   val Loss: 73.8874   time: 3.46s   best: 73.8874\n",
      "Epoch 742/1000:  train Loss: 75.6635   val Loss: 74.0788   time: 3.43s   best: 73.8874\n",
      "Epoch 743/1000:  train Loss: 75.6805   val Loss: 73.9666   time: 3.43s   best: 73.8874\n",
      "Epoch 744/1000:  train Loss: 75.6419   val Loss: 73.8941   time: 3.41s   best: 73.8874\n",
      "Epoch 745/1000:  train Loss: 75.6157   val Loss: 73.9909   time: 3.41s   best: 73.8874\n",
      "Epoch 746/1000:  train Loss: 75.6242   val Loss: 73.8629   time: 3.47s   best: 73.8629\n",
      "Epoch 747/1000:  train Loss: 75.5875   val Loss: 74.0894   time: 3.58s   best: 73.8629\n",
      "Epoch 748/1000:  train Loss: 75.5710   val Loss: 73.9496   time: 3.50s   best: 73.8629\n",
      "Epoch 749/1000:  train Loss: 75.5006   val Loss: 73.9176   time: 3.43s   best: 73.8629\n",
      "Epoch 750/1000:  train Loss: 75.5370   val Loss: 74.0198   time: 3.43s   best: 73.8629\n",
      "Epoch 751/1000:  train Loss: 75.5290   val Loss: 74.1305   time: 3.41s   best: 73.8629\n",
      "Epoch 752/1000:  train Loss: 75.5098   val Loss: 73.9776   time: 3.41s   best: 73.8629\n",
      "Epoch 753/1000:  train Loss: 75.5420   val Loss: 73.8180   time: 3.43s   best: 73.8180\n",
      "Epoch 754/1000:  train Loss: 75.5303   val Loss: 73.9049   time: 3.57s   best: 73.8180\n",
      "Epoch 755/1000:  train Loss: 75.5694   val Loss: 73.9854   time: 3.54s   best: 73.8180\n",
      "Epoch 756/1000:  train Loss: 75.6585   val Loss: 73.9913   time: 3.44s   best: 73.8180\n",
      "Epoch 757/1000:  train Loss: 75.5017   val Loss: 74.2600   time: 3.44s   best: 73.8180\n",
      "Epoch 758/1000:  train Loss: 75.5347   val Loss: 74.0443   time: 3.42s   best: 73.8180\n",
      "Epoch 759/1000:  train Loss: 75.5266   val Loss: 73.9194   time: 3.41s   best: 73.8180\n",
      "Epoch 760/1000:  train Loss: 75.4784   val Loss: 73.9941   time: 3.42s   best: 73.8180\n",
      "Epoch 761/1000:  train Loss: 75.4336   val Loss: 73.9288   time: 3.55s   best: 73.8180\n",
      "Epoch 762/1000:  train Loss: 75.5927   val Loss: 73.8746   time: 3.56s   best: 73.8180\n",
      "Epoch 763/1000:  train Loss: 75.4819   val Loss: 73.9371   time: 3.45s   best: 73.8180\n",
      "Epoch 764/1000:  train Loss: 75.4661   val Loss: 74.0444   time: 3.43s   best: 73.8180\n",
      "Epoch 765/1000:  train Loss: 75.5550   val Loss: 73.8123   time: 3.42s   best: 73.8123\n",
      "Epoch 766/1000:  train Loss: 75.4577   val Loss: 73.7906   time: 3.41s   best: 73.7906\n",
      "Epoch 767/1000:  train Loss: 75.4785   val Loss: 73.8485   time: 3.42s   best: 73.7906\n",
      "Epoch 768/1000:  train Loss: 75.3574   val Loss: 73.8754   time: 3.52s   best: 73.7906\n",
      "Epoch 769/1000:  train Loss: 75.3591   val Loss: 73.8519   time: 3.57s   best: 73.7906\n",
      "Epoch 770/1000:  train Loss: 75.3515   val Loss: 73.9644   time: 3.48s   best: 73.7906\n",
      "Epoch 771/1000:  train Loss: 75.4384   val Loss: 73.7115   time: 3.44s   best: 73.7115\n",
      "Epoch 772/1000:  train Loss: 75.4065   val Loss: 73.8156   time: 3.43s   best: 73.7115\n",
      "Epoch 773/1000:  train Loss: 75.3843   val Loss: 73.8988   time: 3.41s   best: 73.7115\n",
      "Epoch 774/1000:  train Loss: 75.3978   val Loss: 73.7929   time: 3.42s   best: 73.7115\n",
      "Epoch 775/1000:  train Loss: 75.4721   val Loss: 73.8729   time: 3.48s   best: 73.7115\n",
      "Epoch 776/1000:  train Loss: 75.4476   val Loss: 73.7443   time: 3.57s   best: 73.7115\n",
      "Epoch 777/1000:  train Loss: 75.4694   val Loss: 73.7893   time: 3.50s   best: 73.7115\n",
      "Epoch 778/1000:  train Loss: 75.4168   val Loss: 74.2278   time: 3.43s   best: 73.7115\n",
      "Epoch 779/1000:  train Loss: 75.3520   val Loss: 73.8529   time: 3.43s   best: 73.7115\n",
      "Epoch 780/1000:  train Loss: 75.2803   val Loss: 73.7719   time: 3.41s   best: 73.7115\n",
      "Epoch 781/1000:  train Loss: 75.3076   val Loss: 73.8495   time: 3.41s   best: 73.7115\n",
      "Epoch 782/1000:  train Loss: 75.2899   val Loss: 73.8005   time: 3.46s   best: 73.7115\n",
      "Epoch 783/1000:  train Loss: 75.3802   val Loss: 73.9407   time: 3.57s   best: 73.7115\n",
      "Epoch 784/1000:  train Loss: 75.2922   val Loss: 73.7316   time: 3.51s   best: 73.7115\n",
      "Epoch 785/1000:  train Loss: 75.3884   val Loss: 73.7849   time: 3.43s   best: 73.7115\n",
      "Epoch 786/1000:  train Loss: 75.3904   val Loss: 73.6889   time: 3.43s   best: 73.6889\n",
      "Epoch 787/1000:  train Loss: 75.3130   val Loss: 73.7787   time: 3.42s   best: 73.6889\n",
      "Epoch 788/1000:  train Loss: 75.4247   val Loss: 73.8995   time: 3.41s   best: 73.6889\n",
      "Epoch 789/1000:  train Loss: 75.2524   val Loss: 73.7398   time: 3.44s   best: 73.6889\n",
      "Epoch 790/1000:  train Loss: 75.2804   val Loss: 73.6773   time: 3.57s   best: 73.6773\n",
      "Epoch 791/1000:  train Loss: 75.3028   val Loss: 73.6274   time: 3.53s   best: 73.6274\n",
      "Epoch 792/1000:  train Loss: 75.2465   val Loss: 73.6539   time: 3.44s   best: 73.6274\n",
      "Epoch 793/1000:  train Loss: 75.3157   val Loss: 73.9325   time: 3.44s   best: 73.6274\n",
      "Epoch 794/1000:  train Loss: 75.2246   val Loss: 73.7016   time: 3.42s   best: 73.6274\n",
      "Epoch 795/1000:  train Loss: 75.2398   val Loss: 73.5786   time: 3.41s   best: 73.5786\n",
      "Epoch 796/1000:  train Loss: 75.1711   val Loss: 73.6668   time: 3.43s   best: 73.5786\n",
      "Epoch 797/1000:  train Loss: 75.2270   val Loss: 73.7849   time: 3.54s   best: 73.5786\n",
      "Epoch 798/1000:  train Loss: 75.2099   val Loss: 73.6983   time: 3.56s   best: 73.5786\n",
      "Epoch 799/1000:  train Loss: 75.1641   val Loss: 73.7834   time: 3.46s   best: 73.5786\n",
      "Epoch 800/1000:  train Loss: 75.1666   val Loss: 73.5687   time: 3.43s   best: 73.5687\n",
      "Epoch 801/1000:  train Loss: 75.1798   val Loss: 73.8089   time: 3.43s   best: 73.5687\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 802/1000:  train Loss: 75.2242   val Loss: 73.6350   time: 3.42s   best: 73.5687\n",
      "Epoch 803/1000:  train Loss: 75.1513   val Loss: 73.7108   time: 3.42s   best: 73.5687\n",
      "Epoch 804/1000:  train Loss: 75.1922   val Loss: 73.8288   time: 3.51s   best: 73.5687\n",
      "Epoch 805/1000:  train Loss: 75.1846   val Loss: 73.6496   time: 3.57s   best: 73.5687\n",
      "Epoch 806/1000:  train Loss: 75.1473   val Loss: 73.5429   time: 3.49s   best: 73.5429\n",
      "Epoch 807/1000:  train Loss: 75.1691   val Loss: 73.6535   time: 3.43s   best: 73.5429\n",
      "Epoch 808/1000:  train Loss: 75.1445   val Loss: 73.5958   time: 3.43s   best: 73.5429\n",
      "Epoch 809/1000:  train Loss: 75.2006   val Loss: 73.5564   time: 3.41s   best: 73.5429\n",
      "Epoch 810/1000:  train Loss: 75.1101   val Loss: 73.5600   time: 3.41s   best: 73.5429\n",
      "Epoch 811/1000:  train Loss: 75.1208   val Loss: 73.6549   time: 3.45s   best: 73.5429\n",
      "Epoch 812/1000:  train Loss: 75.0834   val Loss: 73.5205   time: 3.57s   best: 73.5205\n",
      "Epoch 813/1000:  train Loss: 75.0883   val Loss: 73.6050   time: 3.54s   best: 73.5205\n",
      "Epoch 814/1000:  train Loss: 75.0511   val Loss: 73.6598   time: 3.44s   best: 73.5205\n",
      "Epoch 815/1000:  train Loss: 75.1813   val Loss: 73.5859   time: 3.44s   best: 73.5205\n",
      "Epoch 816/1000:  train Loss: 75.0442   val Loss: 73.5590   time: 3.43s   best: 73.5205\n",
      "Epoch 817/1000:  train Loss: 75.0481   val Loss: 73.7137   time: 3.42s   best: 73.5205\n",
      "Epoch 818/1000:  train Loss: 75.1095   val Loss: 73.5568   time: 3.44s   best: 73.5205\n",
      "Epoch 819/1000:  train Loss: 75.2202   val Loss: 73.5621   time: 3.56s   best: 73.5205\n",
      "Epoch 820/1000:  train Loss: 75.0772   val Loss: 73.7237   time: 3.55s   best: 73.5205\n",
      "Epoch 821/1000:  train Loss: 75.0849   val Loss: 73.4595   time: 3.45s   best: 73.4595\n",
      "Epoch 822/1000:  train Loss: 75.0635   val Loss: 73.7797   time: 3.44s   best: 73.4595\n",
      "Epoch 823/1000:  train Loss: 74.9906   val Loss: 74.2543   time: 3.42s   best: 73.4595\n",
      "Epoch 824/1000:  train Loss: 74.9991   val Loss: 73.5723   time: 3.41s   best: 73.4595\n",
      "Epoch 825/1000:  train Loss: 75.0265   val Loss: 73.5486   time: 3.42s   best: 73.4595\n",
      "Epoch 826/1000:  train Loss: 75.0519   val Loss: 73.5966   time: 3.52s   best: 73.4595\n",
      "Epoch 827/1000:  train Loss: 75.0384   val Loss: 73.6228   time: 3.57s   best: 73.4595\n",
      "Epoch 828/1000:  train Loss: 75.0099   val Loss: 73.6809   time: 3.47s   best: 73.4595\n",
      "Epoch 829/1000:  train Loss: 75.0300   val Loss: 73.6172   time: 3.43s   best: 73.4595\n",
      "Epoch 830/1000:  train Loss: 75.0254   val Loss: 73.5819   time: 3.43s   best: 73.4595\n",
      "Epoch 831/1000:  train Loss: 74.9817   val Loss: 73.5636   time: 3.41s   best: 73.4595\n",
      "Epoch 832/1000:  train Loss: 74.9883   val Loss: 73.5440   time: 3.41s   best: 73.4595\n",
      "Epoch 833/1000:  train Loss: 74.9896   val Loss: 73.6899   time: 3.48s   best: 73.4595\n",
      "Epoch 834/1000:  train Loss: 74.9718   val Loss: 73.6263   time: 3.57s   best: 73.4595\n",
      "Epoch 835/1000:  train Loss: 75.0126   val Loss: 73.5604   time: 3.51s   best: 73.4595\n",
      "Epoch 836/1000:  train Loss: 74.9883   val Loss: 73.3875   time: 3.43s   best: 73.3875\n",
      "Epoch 837/1000:  train Loss: 74.8773   val Loss: 73.5221   time: 3.44s   best: 73.3875\n",
      "Epoch 838/1000:  train Loss: 74.9371   val Loss: 73.4091   time: 3.42s   best: 73.3875\n",
      "Epoch 839/1000:  train Loss: 74.9463   val Loss: 73.5171   time: 3.42s   best: 73.3875\n",
      "Epoch 840/1000:  train Loss: 74.9545   val Loss: 73.6657   time: 3.46s   best: 73.3875\n",
      "Epoch 841/1000:  train Loss: 74.9108   val Loss: 73.5362   time: 3.59s   best: 73.3875\n",
      "Epoch 842/1000:  train Loss: 74.9858   val Loss: 73.6644   time: 3.53s   best: 73.3875\n",
      "Epoch 843/1000:  train Loss: 74.8853   val Loss: 73.5999   time: 3.44s   best: 73.3875\n",
      "Epoch 844/1000:  train Loss: 74.9197   val Loss: 73.4293   time: 3.44s   best: 73.3875\n",
      "Epoch 845/1000:  train Loss: 74.8785   val Loss: 73.4274   time: 3.42s   best: 73.3875\n",
      "Epoch 846/1000:  train Loss: 74.8976   val Loss: 73.7459   time: 3.42s   best: 73.3875\n",
      "Epoch 847/1000:  train Loss: 74.9618   val Loss: 73.6377   time: 3.44s   best: 73.3875\n",
      "Epoch 848/1000:  train Loss: 74.8957   val Loss: 73.6128   time: 3.57s   best: 73.3875\n",
      "Epoch 849/1000:  train Loss: 74.9670   val Loss: 73.3666   time: 3.55s   best: 73.3666\n",
      "Epoch 850/1000:  train Loss: 74.8692   val Loss: 73.4307   time: 3.45s   best: 73.3666\n",
      "Epoch 851/1000:  train Loss: 74.8802   val Loss: 73.4585   time: 3.44s   best: 73.3666\n",
      "Epoch 852/1000:  train Loss: 74.9160   val Loss: 73.4399   time: 3.43s   best: 73.3666\n",
      "Epoch 853/1000:  train Loss: 74.8830   val Loss: 73.3969   time: 3.42s   best: 73.3666\n",
      "Epoch 854/1000:  train Loss: 74.8246   val Loss: 73.4292   time: 3.42s   best: 73.3666\n",
      "Epoch 855/1000:  train Loss: 74.9012   val Loss: 73.4097   time: 3.53s   best: 73.3666\n",
      "Epoch 856/1000:  train Loss: 74.8211   val Loss: 73.3894   time: 3.56s   best: 73.3666\n",
      "Epoch 857/1000:  train Loss: 74.7856   val Loss: 73.3850   time: 3.47s   best: 73.3666\n",
      "Epoch 858/1000:  train Loss: 74.8200   val Loss: 73.3567   time: 3.44s   best: 73.3567\n",
      "Epoch 859/1000:  train Loss: 74.8257   val Loss: 73.4388   time: 3.43s   best: 73.3567\n",
      "Epoch 860/1000:  train Loss: 74.7709   val Loss: 73.2933   time: 3.42s   best: 73.2933\n",
      "Epoch 861/1000:  train Loss: 74.8032   val Loss: 73.3488   time: 3.42s   best: 73.2933\n",
      "Epoch 862/1000:  train Loss: 74.8619   val Loss: 73.3735   time: 3.50s   best: 73.2933\n",
      "Epoch 863/1000:  train Loss: 74.8210   val Loss: 73.3711   time: 3.58s   best: 73.2933\n",
      "Epoch 864/1000:  train Loss: 74.7773   val Loss: 73.3817   time: 3.49s   best: 73.2933\n",
      "Epoch 865/1000:  train Loss: 74.8199   val Loss: 73.5146   time: 3.44s   best: 73.2933\n",
      "Epoch 866/1000:  train Loss: 74.8469   val Loss: 73.6717   time: 3.43s   best: 73.2933\n",
      "Epoch 867/1000:  train Loss: 74.7874   val Loss: 73.3754   time: 3.42s   best: 73.2933\n",
      "Epoch 868/1000:  train Loss: 74.7194   val Loss: 73.4377   time: 3.41s   best: 73.2933\n",
      "Epoch 869/1000:  train Loss: 74.8039   val Loss: 73.3004   time: 3.48s   best: 73.2933\n",
      "Epoch 870/1000:  train Loss: 74.7996   val Loss: 73.4702   time: 3.58s   best: 73.2933\n",
      "Epoch 871/1000:  train Loss: 74.7345   val Loss: 73.3667   time: 3.50s   best: 73.2933\n",
      "Epoch 872/1000:  train Loss: 74.7539   val Loss: 73.5646   time: 3.43s   best: 73.2933\n",
      "Epoch 873/1000:  train Loss: 74.7033   val Loss: 73.2940   time: 3.43s   best: 73.2933\n",
      "Epoch 874/1000:  train Loss: 74.7573   val Loss: 73.2366   time: 3.41s   best: 73.2366\n",
      "Epoch 875/1000:  train Loss: 74.6841   val Loss: 73.5644   time: 3.42s   best: 73.2366\n",
      "Epoch 876/1000:  train Loss: 74.6847   val Loss: 73.3204   time: 3.45s   best: 73.2366\n",
      "Epoch 877/1000:  train Loss: 74.7516   val Loss: 73.3995   time: 3.58s   best: 73.2366\n",
      "Epoch 878/1000:  train Loss: 74.7307   val Loss: 73.5526   time: 3.53s   best: 73.2366\n",
      "Epoch 879/1000:  train Loss: 74.7130   val Loss: 73.2658   time: 3.44s   best: 73.2366\n",
      "Epoch 880/1000:  train Loss: 74.7706   val Loss: 73.4441   time: 3.44s   best: 73.2366\n",
      "Epoch 881/1000:  train Loss: 74.7122   val Loss: 73.3072   time: 3.42s   best: 73.2366\n",
      "Epoch 882/1000:  train Loss: 74.6781   val Loss: 73.4823   time: 3.42s   best: 73.2366\n",
      "Epoch 883/1000:  train Loss: 74.7150   val Loss: 73.2822   time: 3.43s   best: 73.2366\n",
      "Epoch 884/1000:  train Loss: 74.8929   val Loss: 73.4548   time: 3.56s   best: 73.2366\n",
      "Epoch 885/1000:  train Loss: 74.6732   val Loss: 73.2530   time: 3.55s   best: 73.2366\n",
      "Epoch 886/1000:  train Loss: 74.6512   val Loss: 73.3043   time: 3.46s   best: 73.2366\n",
      "Epoch 887/1000:  train Loss: 74.7022   val Loss: 73.2513   time: 3.43s   best: 73.2366\n",
      "Epoch 888/1000:  train Loss: 74.6617   val Loss: 73.3451   time: 3.43s   best: 73.2366\n",
      "Epoch 889/1000:  train Loss: 74.6595   val Loss: 73.3654   time: 3.42s   best: 73.2366\n",
      "Epoch 890/1000:  train Loss: 74.7026   val Loss: 73.3608   time: 3.42s   best: 73.2366\n",
      "Epoch 891/1000:  train Loss: 74.7881   val Loss: 73.3799   time: 3.51s   best: 73.2366\n",
      "Epoch 892/1000:  train Loss: 74.7056   val Loss: 73.3299   time: 3.57s   best: 73.2366\n",
      "Epoch 893/1000:  train Loss: 74.5711   val Loss: 73.2866   time: 3.49s   best: 73.2366\n",
      "Epoch 894/1000:  train Loss: 74.6317   val Loss: 73.2661   time: 3.43s   best: 73.2366\n",
      "Epoch 895/1000:  train Loss: 74.5218   val Loss: 73.1733   time: 3.44s   best: 73.1733\n",
      "Epoch 896/1000:  train Loss: 74.6496   val Loss: 73.3375   time: 3.41s   best: 73.1733\n",
      "Epoch 897/1000:  train Loss: 74.5797   val Loss: 73.2117   time: 3.41s   best: 73.1733\n",
      "Epoch 898/1000:  train Loss: 74.6356   val Loss: 73.4709   time: 3.46s   best: 73.1733\n",
      "Epoch 899/1000:  train Loss: 74.5166   val Loss: 73.2388   time: 3.57s   best: 73.1733\n",
      "Epoch 900/1000:  train Loss: 74.5920   val Loss: 73.2345   time: 3.53s   best: 73.1733\n",
      "Epoch 901/1000:  train Loss: 74.5922   val Loss: 73.2143   time: 3.44s   best: 73.1733\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 902/1000:  train Loss: 74.6913   val Loss: 73.2527   time: 3.44s   best: 73.1733\n",
      "Epoch 903/1000:  train Loss: 74.5591   val Loss: 73.3303   time: 3.42s   best: 73.1733\n",
      "Epoch 904/1000:  train Loss: 74.5115   val Loss: 73.1260   time: 3.42s   best: 73.1260\n",
      "Epoch 905/1000:  train Loss: 74.5370   val Loss: 73.2167   time: 3.45s   best: 73.1260\n",
      "Epoch 906/1000:  train Loss: 74.6096   val Loss: 73.5978   time: 3.58s   best: 73.1260\n",
      "Epoch 907/1000:  train Loss: 74.4933   val Loss: 73.2142   time: 3.53s   best: 73.1260\n",
      "Epoch 908/1000:  train Loss: 74.5462   val Loss: 73.2443   time: 3.44s   best: 73.1260\n",
      "Epoch 909/1000:  train Loss: 74.5388   val Loss: 73.2361   time: 3.44s   best: 73.1260\n",
      "Epoch 910/1000:  train Loss: 74.4495   val Loss: 73.1070   time: 3.42s   best: 73.1070\n",
      "Epoch 911/1000:  train Loss: 74.4632   val Loss: 73.2277   time: 3.42s   best: 73.1070\n",
      "Epoch 912/1000:  train Loss: 74.4940   val Loss: 73.2650   time: 3.42s   best: 73.1070\n",
      "Epoch 913/1000:  train Loss: 74.4571   val Loss: 73.1505   time: 3.53s   best: 73.1070\n",
      "Epoch 914/1000:  train Loss: 74.4684   val Loss: 73.1563   time: 3.56s   best: 73.1070\n",
      "Epoch 915/1000:  train Loss: 74.5491   val Loss: 73.1011   time: 3.47s   best: 73.1011\n",
      "Epoch 916/1000:  train Loss: 74.5197   val Loss: 73.4680   time: 3.44s   best: 73.1011\n",
      "Epoch 917/1000:  train Loss: 74.4782   val Loss: 73.2339   time: 3.43s   best: 73.1011\n",
      "Epoch 918/1000:  train Loss: 74.4576   val Loss: 73.2294   time: 3.42s   best: 73.1011\n",
      "Epoch 919/1000:  train Loss: 74.4607   val Loss: 73.2477   time: 3.42s   best: 73.1011\n",
      "Epoch 920/1000:  train Loss: 74.4892   val Loss: 73.3356   time: 3.49s   best: 73.1011\n",
      "Epoch 921/1000:  train Loss: 74.3923   val Loss: 73.2149   time: 3.58s   best: 73.1011\n",
      "Epoch 922/1000:  train Loss: 74.5978   val Loss: 73.2414   time: 3.50s   best: 73.1011\n",
      "Epoch 923/1000:  train Loss: 74.4617   val Loss: 73.2802   time: 3.43s   best: 73.1011\n",
      "Epoch 924/1000:  train Loss: 74.4465   val Loss: 73.1526   time: 3.44s   best: 73.1011\n",
      "Epoch 925/1000:  train Loss: 74.4225   val Loss: 73.2891   time: 3.41s   best: 73.1011\n",
      "Epoch 926/1000:  train Loss: 74.4783   val Loss: 73.1959   time: 3.41s   best: 73.1011\n",
      "Epoch 927/1000:  train Loss: 74.3899   val Loss: 73.3996   time: 3.46s   best: 73.1011\n",
      "Epoch 928/1000:  train Loss: 74.4489   val Loss: 73.0837   time: 3.58s   best: 73.0837\n",
      "Epoch 929/1000:  train Loss: 74.4017   val Loss: 73.0996   time: 3.51s   best: 73.0837\n",
      "Epoch 930/1000:  train Loss: 74.3684   val Loss: 73.2127   time: 3.43s   best: 73.0837\n",
      "Epoch 931/1000:  train Loss: 74.3442   val Loss: 73.0998   time: 3.43s   best: 73.0837\n",
      "Epoch 932/1000:  train Loss: 74.3632   val Loss: 73.0882   time: 3.41s   best: 73.0837\n",
      "Epoch 933/1000:  train Loss: 74.4176   val Loss: 73.2442   time: 3.41s   best: 73.0837\n",
      "Epoch 934/1000:  train Loss: 74.3840   val Loss: 73.0537   time: 3.44s   best: 73.0537\n",
      "Epoch 935/1000:  train Loss: 74.3774   val Loss: 73.0506   time: 3.58s   best: 73.0506\n",
      "Epoch 936/1000:  train Loss: 74.3812   val Loss: 73.2843   time: 3.53s   best: 73.0506\n",
      "Epoch 937/1000:  train Loss: 74.4607   val Loss: 73.7078   time: 3.44s   best: 73.0506\n",
      "Epoch 938/1000:  train Loss: 74.3505   val Loss: 73.1199   time: 3.43s   best: 73.0506\n",
      "Epoch 939/1000:  train Loss: 74.3244   val Loss: 73.0151   time: 3.42s   best: 73.0151\n",
      "Epoch 940/1000:  train Loss: 74.3676   val Loss: 73.0265   time: 3.42s   best: 73.0151\n",
      "Epoch 941/1000:  train Loss: 74.3272   val Loss: 73.1459   time: 3.42s   best: 73.0151\n",
      "Epoch 942/1000:  train Loss: 74.3937   val Loss: 73.0214   time: 3.54s   best: 73.0151\n",
      "Epoch 943/1000:  train Loss: 74.2971   val Loss: 73.1479   time: 3.56s   best: 73.0151\n",
      "Epoch 944/1000:  train Loss: 74.3265   val Loss: 73.0136   time: 3.46s   best: 73.0136\n",
      "Epoch 945/1000:  train Loss: 74.2793   val Loss: 73.2781   time: 3.43s   best: 73.0136\n",
      "Epoch 946/1000:  train Loss: 74.4242   val Loss: 73.1568   time: 3.42s   best: 73.0136\n",
      "Epoch 947/1000:  train Loss: 74.3514   val Loss: 73.2962   time: 3.41s   best: 73.0136\n",
      "Epoch 948/1000:  train Loss: 74.3096   val Loss: 73.2120   time: 3.41s   best: 73.0136\n",
      "Epoch 949/1000:  train Loss: 74.3925   val Loss: 73.0400   time: 3.50s   best: 73.0136\n",
      "Epoch 950/1000:  train Loss: 74.4565   val Loss: 73.2300   time: 3.56s   best: 73.0136\n",
      "Epoch 951/1000:  train Loss: 74.2884   val Loss: 73.1801   time: 3.48s   best: 73.0136\n",
      "Epoch 952/1000:  train Loss: 74.2502   val Loss: 73.1055   time: 3.43s   best: 73.0136\n",
      "Epoch 953/1000:  train Loss: 74.2822   val Loss: 73.0496   time: 3.43s   best: 73.0136\n",
      "Epoch 954/1000:  train Loss: 74.2752   val Loss: 73.2160   time: 3.41s   best: 73.0136\n",
      "Epoch 955/1000:  train Loss: 74.2881   val Loss: 73.0149   time: 3.41s   best: 73.0136\n",
      "Epoch 956/1000:  train Loss: 74.2232   val Loss: 73.0954   time: 3.50s   best: 73.0136\n",
      "Epoch 957/1000:  train Loss: 74.2720   val Loss: 73.1273   time: 3.57s   best: 73.0136\n",
      "Epoch 958/1000:  train Loss: 74.2555   val Loss: 72.9495   time: 3.50s   best: 72.9495\n",
      "Epoch 959/1000:  train Loss: 74.2555   val Loss: 72.9430   time: 3.43s   best: 72.9430\n",
      "Epoch 960/1000:  train Loss: 74.2369   val Loss: 73.0476   time: 3.43s   best: 72.9430\n",
      "Epoch 961/1000:  train Loss: 74.2114   val Loss: 72.9989   time: 3.41s   best: 72.9430\n",
      "Epoch 962/1000:  train Loss: 74.2153   val Loss: 73.1580   time: 3.41s   best: 72.9430\n",
      "Epoch 963/1000:  train Loss: 74.2242   val Loss: 72.8977   time: 3.48s   best: 72.8977\n",
      "Epoch 964/1000:  train Loss: 74.1801   val Loss: 72.9126   time: 3.58s   best: 72.8977\n",
      "Epoch 965/1000:  train Loss: 74.2368   val Loss: 72.8698   time: 3.49s   best: 72.8698\n",
      "Epoch 966/1000:  train Loss: 74.2228   val Loss: 73.0280   time: 3.43s   best: 72.8698\n",
      "Epoch 967/1000:  train Loss: 74.1727   val Loss: 73.0977   time: 3.43s   best: 72.8698\n",
      "Epoch 968/1000:  train Loss: 74.1945   val Loss: 72.9491   time: 3.41s   best: 72.8698\n",
      "Epoch 969/1000:  train Loss: 74.1704   val Loss: 72.9574   time: 3.41s   best: 72.8698\n",
      "Epoch 970/1000:  train Loss: 74.1913   val Loss: 72.9699   time: 3.46s   best: 72.8698\n",
      "Epoch 971/1000:  train Loss: 74.1926   val Loss: 73.1529   time: 3.58s   best: 72.8698\n",
      "Epoch 972/1000:  train Loss: 74.1615   val Loss: 72.9743   time: 3.52s   best: 72.8698\n",
      "Epoch 973/1000:  train Loss: 74.2100   val Loss: 72.9556   time: 3.43s   best: 72.8698\n",
      "Epoch 974/1000:  train Loss: 74.2319   val Loss: 72.9621   time: 3.43s   best: 72.8698\n",
      "Epoch 975/1000:  train Loss: 74.2905   val Loss: 73.0207   time: 3.41s   best: 72.8698\n",
      "Epoch 976/1000:  train Loss: 74.1371   val Loss: 72.8804   time: 3.41s   best: 72.8698\n",
      "Epoch 977/1000:  train Loss: 74.0390   val Loss: 72.9322   time: 3.43s   best: 72.8698\n",
      "Epoch 978/1000:  train Loss: 74.2309   val Loss: 73.1207   time: 3.57s   best: 72.8698\n",
      "Epoch 979/1000:  train Loss: 74.1432   val Loss: 72.9737   time: 3.54s   best: 72.8698\n",
      "Epoch 980/1000:  train Loss: 74.1046   val Loss: 72.9390   time: 3.44s   best: 72.8698\n",
      "Epoch 981/1000:  train Loss: 74.2123   val Loss: 72.9623   time: 3.43s   best: 72.8698\n",
      "Epoch 982/1000:  train Loss: 74.1544   val Loss: 73.1837   time: 3.41s   best: 72.8698\n",
      "Epoch 983/1000:  train Loss: 74.1613   val Loss: 72.9306   time: 3.41s   best: 72.8698\n",
      "Epoch 984/1000:  train Loss: 74.1167   val Loss: 72.9718   time: 3.42s   best: 72.8698\n",
      "Epoch 985/1000:  train Loss: 74.1323   val Loss: 72.9064   time: 3.54s   best: 72.8698\n",
      "Epoch 986/1000:  train Loss: 74.1350   val Loss: 72.9823   time: 3.55s   best: 72.8698\n",
      "Epoch 987/1000:  train Loss: 74.1092   val Loss: 73.0700   time: 3.45s   best: 72.8698\n",
      "Epoch 988/1000:  train Loss: 74.1362   val Loss: 72.9538   time: 3.43s   best: 72.8698\n",
      "Epoch 989/1000:  train Loss: 74.0787   val Loss: 72.8646   time: 3.42s   best: 72.8646\n",
      "Epoch 990/1000:  train Loss: 74.1104   val Loss: 72.9986   time: 3.42s   best: 72.8646\n",
      "Epoch 991/1000:  train Loss: 74.1669   val Loss: 73.1660   time: 3.42s   best: 72.8646\n",
      "Epoch 992/1000:  train Loss: 74.0992   val Loss: 72.8794   time: 3.54s   best: 72.8646\n",
      "Epoch 993/1000:  train Loss: 74.1114   val Loss: 72.8289   time: 3.57s   best: 72.8289\n",
      "Epoch 994/1000:  train Loss: 74.0949   val Loss: 72.8824   time: 3.47s   best: 72.8289\n",
      "Epoch 995/1000:  train Loss: 74.0857   val Loss: 72.9276   time: 3.43s   best: 72.8289\n",
      "Epoch 996/1000:  train Loss: 74.0584   val Loss: 72.8509   time: 3.43s   best: 72.8289\n",
      "Epoch 997/1000:  train Loss: 74.0161   val Loss: 72.9912   time: 3.41s   best: 72.8289\n",
      "Epoch 998/1000:  train Loss: 74.1195   val Loss: 73.0838   time: 3.41s   best: 72.8289\n",
      "Epoch 999/1000:  train Loss: 74.0623   val Loss: 72.8939   time: 3.47s   best: 72.8289\n",
      "Epoch 1000/1000:  train Loss: 74.0543   val Loss: 72.9093   time: 3.58s   best: 72.8289\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete in 57m 42s   best validation loss: 72.8289\n"
     ]
    }
   ],
   "source": [
    "bl_dict={'train':train_bl2, 'val':val_bl2}\n",
    "log_file = 'log_test2.txt'\n",
    "\n",
    "losses, best_model = train_model(model2, criterion, optimizer , max_lr=0.001, dataloader=bl_dict,\n",
    "                                 num_epochs=1000, logFile=log_file, log_every=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 43/43 [00:00<00:00, 364.13it/s]\n",
      "100%|██████████| 51/51 [00:00<00:00, 326.36it/s]\n",
      "100%|██████████| 396/396 [00:01<00:00, 307.99it/s]\n",
      "100%|██████████| 3/3 [00:00<00:00, 63.60it/s]\n"
     ]
    }
   ],
   "source": [
    "%run utils_Tiling2_RLSTM.py\n",
    "val_df2 = get_results_df(train_val_dataset2, val_bl2, val_indices2, model2)\n",
    "test_df2 = get_results_df(test_dataset2, test_bl2, test_indices2, model2)\n",
    "train_df2 = get_results_df(train_val_dataset2, train_bl2, train_indices2, model2)\n",
    "benchmark_df2 = get_results_df(bench_dataset2, bench_bl2, bench_indices2, model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    270724\n",
      "           1       0.75      0.63      0.69    160708\n",
      "           2       0.54      0.25      0.34     47769\n",
      "           3       0.41      0.68      0.51     35923\n",
      "           4       0.55      0.34      0.42     87262\n",
      "           5       0.40      0.22      0.28     25977\n",
      "           6       0.46      0.27      0.34     17516\n",
      "           7       0.50      0.89      0.64    113715\n",
      "           8       0.56      0.32      0.41     27751\n",
      "           9       0.46      0.54      0.50     19359\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "          28       1.00      1.00      1.00         0\n",
      "          29       1.00      1.00      1.00         0\n",
      "          30       1.00      1.00      1.00         0\n",
      "          31       1.00      1.00      1.00         0\n",
      "          32       1.00      1.00      1.00         0\n",
      "          33       1.00      1.00      1.00         0\n",
      "          34       1.00      1.00      1.00         0\n",
      "          35       1.00      1.00      1.00         0\n",
      "          36       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.71      0.71      0.71    806704\n",
      "   macro avg       0.88      0.87      0.87    806704\n",
      "weighted avg       0.72      0.71      0.69    806704\n",
      " samples avg       0.71      0.71      0.71    806704\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     28536\n",
      "           1       0.73      0.61      0.67     17081\n",
      "           2       0.54      0.24      0.34      5340\n",
      "           3       0.39      0.67      0.50      4038\n",
      "           4       0.53      0.32      0.40      9453\n",
      "           5       0.36      0.19      0.25      2872\n",
      "           6       0.46      0.27      0.34      1994\n",
      "           7       0.49      0.89      0.64     12489\n",
      "           8       0.53      0.28      0.37      2900\n",
      "           9       0.44      0.49      0.46      2000\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "          28       1.00      1.00      1.00         0\n",
      "          29       1.00      1.00      1.00         0\n",
      "          30       1.00      1.00      1.00         0\n",
      "          31       1.00      1.00      1.00         0\n",
      "          32       1.00      1.00      1.00         0\n",
      "          33       1.00      1.00      1.00         0\n",
      "          34       1.00      1.00      1.00         0\n",
      "          35       1.00      1.00      1.00         0\n",
      "          36       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.69      0.69      0.69     86703\n",
      "   macro avg       0.88      0.86      0.86     86703\n",
      "weighted avg       0.70      0.69      0.68     86703\n",
      " samples avg       0.69      0.69      0.69     86703\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     32837\n",
      "           1       0.73      0.62      0.67     19684\n",
      "           2       0.50      0.23      0.32      5715\n",
      "           3       0.39      0.65      0.49      4402\n",
      "           4       0.53      0.33      0.41     10648\n",
      "           5       0.39      0.20      0.27      3261\n",
      "           6       0.44      0.26      0.32      2189\n",
      "           7       0.50      0.88      0.63     14194\n",
      "           8       0.54      0.30      0.39      3421\n",
      "           9       0.44      0.50      0.47      2413\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "          28       1.00      1.00      1.00         0\n",
      "          29       1.00      1.00      1.00         0\n",
      "          30       1.00      1.00      1.00         0\n",
      "          31       1.00      1.00      1.00         0\n",
      "          32       1.00      1.00      1.00         0\n",
      "          33       1.00      1.00      1.00         0\n",
      "          34       1.00      1.00      1.00         0\n",
      "          35       1.00      1.00      1.00         0\n",
      "          36       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.69      0.69      0.69     98764\n",
      "   macro avg       0.88      0.86      0.86     98764\n",
      "weighted avg       0.70      0.69      0.68     98764\n",
      " samples avg       0.69      0.69      0.69     98764\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       156\n",
      "           1       0.52      0.65      0.58       112\n",
      "           2       0.20      0.06      0.09        35\n",
      "           3       0.38      0.54      0.45        24\n",
      "           4       0.23      0.17      0.20        63\n",
      "           5       0.10      0.04      0.05        28\n",
      "           6       0.50      0.10      0.17        10\n",
      "           7       0.48      0.76      0.58        82\n",
      "           8       0.22      0.10      0.14        20\n",
      "           9       0.50      0.24      0.32        17\n",
      "          10       1.00      1.00      1.00         0\n",
      "          11       1.00      1.00      1.00         0\n",
      "          12       1.00      1.00      1.00         0\n",
      "          13       1.00      1.00      1.00         0\n",
      "          14       1.00      1.00      1.00         0\n",
      "          15       1.00      1.00      1.00         0\n",
      "          16       1.00      1.00      1.00         0\n",
      "          17       1.00      1.00      1.00         0\n",
      "          18       1.00      1.00      1.00         0\n",
      "          19       1.00      1.00      1.00         0\n",
      "          20       1.00      1.00      1.00         0\n",
      "          21       1.00      1.00      1.00         0\n",
      "          22       1.00      1.00      1.00         0\n",
      "          23       1.00      1.00      1.00         0\n",
      "          24       1.00      1.00      1.00         0\n",
      "          25       1.00      1.00      1.00         0\n",
      "          26       1.00      1.00      1.00         0\n",
      "          27       1.00      1.00      1.00         0\n",
      "          28       1.00      1.00      1.00         0\n",
      "          29       1.00      1.00      1.00         0\n",
      "          30       1.00      1.00      1.00         0\n",
      "          31       1.00      1.00      1.00         0\n",
      "          32       1.00      1.00      1.00         0\n",
      "          33       1.00      1.00      1.00         0\n",
      "          34       1.00      1.00      1.00         0\n",
      "          35       1.00      1.00      1.00         0\n",
      "          36       1.00      1.00      1.00         0\n",
      "\n",
      "   micro avg       0.59      0.59      0.59       547\n",
      "   macro avg       0.84      0.83      0.83       547\n",
      "weighted avg       0.56      0.59      0.56       547\n",
      " samples avg       0.59      0.59      0.59       547\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report(train_df2)\n",
    "report(val_df2)\n",
    "report(test_df2)\n",
    "report(benchmark_df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model2.state_dict(), 'Models/2ndModel_final_correct.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of the models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('Models/1stModel_final.pkl',map_location=train_device))\n",
    "model.to(train_device)\n",
    "print()\n",
    "\n",
    "model2.load_state_dict(torch.load('Models/2ndModel_final_correct.pkl',map_location=train_device))\n",
    "model2.to(train_device)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modèle</th>\n",
       "      <th>Ensemble d'entraînement</th>\n",
       "      <th>Ensemble de validation</th>\n",
       "      <th>Ensemble de tests</th>\n",
       "      <th>Benchmark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1er modèle du tiling</td>\n",
       "      <td>87.246915</td>\n",
       "      <td>85.556641</td>\n",
       "      <td>85.636398</td>\n",
       "      <td>61.756374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2eme modèle du tiling</td>\n",
       "      <td>70.566032</td>\n",
       "      <td>69.194838</td>\n",
       "      <td>69.440282</td>\n",
       "      <td>59.414991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Modèle  Ensemble d'entraînement  Ensemble de validation  \\\n",
       "0   1er modèle du tiling                87.246915               85.556641   \n",
       "1  2eme modèle du tiling                70.566032               69.194838   \n",
       "\n",
       "   Ensemble de tests  Benchmark  \n",
       "0          85.636398  61.756374  \n",
       "1          69.440282  59.414991  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results1 = { 'Modèle' : '1er modèle du tiling',\n",
    "            'Ensemble d\\'entraînement' : accuracy(train_df), \n",
    "           \"Ensemble de validation\" : accuracy(val_df), \n",
    "           \"Ensemble de tests\" : accuracy(test_df), \n",
    "           \"Benchmark\" : accuracy(benchmark_df)}\n",
    "\n",
    "results2 = { 'Modèle' : '2eme modèle du tiling',\n",
    "            'Ensemble d\\'entraînement' : accuracy(train_df2), \n",
    "           \"Ensemble de validation\" : accuracy(val_df2), \n",
    "           \"Ensemble de tests\" : accuracy(test_df2), \n",
    "           \"Benchmark\" : accuracy(benchmark_df2)}\n",
    "\n",
    "df_results = pd.DataFrame(columns = [\"Modèle\", \"Ensemble d'entraînement\", \"Ensemble de validation\", \"Ensemble de tests\", \"Benchmark\"])\n",
    "df_results = df_results.append(results1, ignore_index=True)\n",
    "df_results = df_results.append(results2, ignore_index=True)\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>exec_time</th>\n",
       "      <th>sched_name</th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.114715</td>\n",
       "      <td>0</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.059773</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.170854</td>\n",
       "      <td>5</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.219145</td>\n",
       "      <td>6</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.148901</td>\n",
       "      <td>58</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.343682</td>\n",
       "      <td>2</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>23.858101</td>\n",
       "      <td>60</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.193101</td>\n",
       "      <td>59</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.151040</td>\n",
       "      <td>85</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.373147</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.193601</td>\n",
       "      <td>86</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.268606</td>\n",
       "      <td>87</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  exec_time  sched_name  \\\n",
       "62  function_blur_MEDIUM   0.114715           0   \n",
       "63  function_blur_MEDIUM   0.059773           4   \n",
       "64  function_blur_MEDIUM   0.170854           5   \n",
       "65  function_blur_MEDIUM   0.219145           6   \n",
       "66  function_blur_MEDIUM   0.148901          58   \n",
       "67  function_blur_MEDIUM   0.343682           2   \n",
       "68  function_blur_MEDIUM  23.858101          60   \n",
       "69  function_blur_MEDIUM   0.193101          59   \n",
       "70  function_blur_MEDIUM   0.151040          85   \n",
       "71  function_blur_MEDIUM   0.373147           3   \n",
       "72  function_blur_MEDIUM   0.193601          86   \n",
       "73  function_blur_MEDIUM   0.268606          87   \n",
       "\n",
       "                                           prediction  \\\n",
       "62  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "63  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "64  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "65  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "66  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "67  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "68  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "69  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "70  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "71  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "72  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "73  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                               target  \n",
       "62  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "63  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "64  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "65  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "66  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "67  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "68  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "69  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "70  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "71  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "72  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "73  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_df[benchmark_df['name']==\"function_blur_MEDIUM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prog_name</th>\n",
       "      <th>exec_time</th>\n",
       "      <th>sched_name</th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.114715</td>\n",
       "      <td>0</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.059773</td>\n",
       "      <td>4</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.114715</td>\n",
       "      <td>0</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.170854</td>\n",
       "      <td>5</td>\n",
       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.219145</td>\n",
       "      <td>6</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.148901</td>\n",
       "      <td>58</td>\n",
       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.343682</td>\n",
       "      <td>2</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>23.858101</td>\n",
       "      <td>60</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.193101</td>\n",
       "      <td>59</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.151040</td>\n",
       "      <td>85</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.373147</td>\n",
       "      <td>3</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.193601</td>\n",
       "      <td>86</td>\n",
       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.268606</td>\n",
       "      <td>87</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               prog_name  exec_time  sched_name  \\\n",
       "67  function_blur_MEDIUM   0.114715           0   \n",
       "68  function_blur_MEDIUM   0.059773           4   \n",
       "69  function_blur_MEDIUM   0.114715           0   \n",
       "70  function_blur_MEDIUM   0.170854           5   \n",
       "71  function_blur_MEDIUM   0.219145           6   \n",
       "72  function_blur_MEDIUM   0.148901          58   \n",
       "73  function_blur_MEDIUM   0.343682           2   \n",
       "74  function_blur_MEDIUM  23.858101          60   \n",
       "75  function_blur_MEDIUM   0.193101          59   \n",
       "76  function_blur_MEDIUM   0.151040          85   \n",
       "77  function_blur_MEDIUM   0.373147           3   \n",
       "78  function_blur_MEDIUM   0.193601          86   \n",
       "79  function_blur_MEDIUM   0.268606          87   \n",
       "\n",
       "                                           prediction  \\\n",
       "67  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "68  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "69  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "70  [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "71  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...   \n",
       "72  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "73  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "74  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...   \n",
       "75  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...   \n",
       "76  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...   \n",
       "77  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "78  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "79  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...   \n",
       "\n",
       "                                               target  \n",
       "67  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "68  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "69  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "70  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "71  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "72  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "73  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "74  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "75  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "76  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "77  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "78  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "79  [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark_df2[benchmark_df2['prog_name']==\"function_blur_MEDIUM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
